reenable cudnn sdpa

Lightning-AI · Jun 24, 2024 · 5f65e50 · 5f65e50
1 parent 4ce822b
commit 5f65e50
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diff --git a/thunder/tests/opinfos.py b/thunder/tests/opinfos.py
@@ -7880,15 +7880,6 @@ def grad_scaled_dot_product_attention_sample_generator(op, device, dtype, requir
     # NOTE: NotImplementedError: Could not run 'aten::_scaled_dot_product_efficient_attention' with arguments from the 'CPU' backend.
     # NOTE: NotImplementedError: Could not run 'aten::_scaled_dot_product_efficient_attention_backward' with arguments from the 'CPU' backend
     devicetypes=(devices.DeviceType.CUDA,),
-    test_directives=(
-        DecorateInfo(
-            pytest.mark.skip(reason="https://github.com/Lightning-AI/lightning-thunder/issues/567"),
-            "test_core_vs_torch_consistency",
-            dtypes=(datatypes.bfloat16, datatypes.float16, datatypes.float32),
-            devicetypes=(devices.DeviceType.CUDA,),
-            active_if=version_between(torch.__version__, min_ver="2.4.0a0", max_ver="2.4.0a99"),
-        ),
-    ),
 )
 nn_ops.append(grad_sdpa_opinfo)
 

diff --git a/thunder/tests/test_cudnn_executor.py b/thunder/tests/test_cudnn_executor.py
@@ -200,10 +200,6 @@ def test_cudnn_vs_torch_consistency(op, device, dtype, *_):
     LooseVersion(cudnn.backend_version_string()) < LooseVersion("8.9.5"),
     reason="cuDNN is required to be at least `8.9.5`",
 )
-@pytest.mark.skipif(
-    version_between(torch.__version__, min_ver="2.4.0a0", max_ver="2.4.0a99"),
-    reason="https://github.com/Lightning-AI/lightning-thunder/issues/567",
-)
 @pytest.mark.parametrize("may_cat_grad_qkv", (True, False), ids=("may-cat-grad-qkv", "never-cat-grad-qkv"))
 @pytest.mark.parametrize("dtype", grad_sdpa_cudnn_opinfo.dtypes(), ids=tuple(map(str, grad_sdpa_cudnn_opinfo.dtypes())))
 def test_vjp_correctness_cudnn_sdpa(dtype, may_cat_grad_qkv):

diff --git a/thunder/tests/test_grad.py b/thunder/tests/test_grad.py
@@ -541,21 +541,12 @@ def test_vjp_correctness_index_put_manual(op, device, dtype, executor, comp):
 
 # NOTE Scaled_Dot_Product_Efficient_Attention_Backward does not support fp64 dtypes
 # RuntimeError: Only fp32, half & bf16 supported at the moment
-@pytest.mark.skipif(
-    not version_between(torch.__version__, min_ver="2.4.0a0", max_ver="2.4.0a99"),
-    reason="https://github.com/Lightning-AI/lightning-thunder/issues/567",
-)
 @ops(
     (get_opinfo("grad_forward_scaled_dot_product_attention"),),
     supported_dtypes=(dtypes.float16, dtypes.bfloat16),
     supported_devicetypes=(devices.DeviceType.CUDA,),
 )
 def test_vjp_correctness_sdpa_manual(op, device, dtype, executor, comp):
-    if version_between(torch.__version__, min_ver="2.4.0a0", max_ver="2.4.0a99"):
-        raise pytest.skip(
-            "https://github.com/Lightning-AI/lightning-thunder/issues/567",
-        )
-
     for sample in op.sample_inputs(device, dtype, requires_grad=True):
         from thunder.executors.sdpaex import sdpa_ex