Makes cudnn a default executor

README.md

@@ -73,14 +73,23 @@ The easiest way to get started with Thunder, requiring no extra installations or
 
 ## Install Thunder
 
-To use Thunder on your local machine, first install [nvFuser](https://github.com/NVIDIA/Fuser) nightly and PyTorch nightly together as follows:
+To use Thunder on your local machine:
+
+- install [nvFuser](https://github.com/NVIDIA/Fuser) nightly and PyTorch nightly together as follows:
 
 ```bash
 # install nvFuser which installs the matching nightly PyTorch
 pip install --pre 'nvfuser-cu121[torch]' --extra-index-url https://pypi.nvidia.com
 ```
 
-Then, install Thunder as follows:
+- install [cudnn](https://gitlab-master.nvidia.com/cudnn/cudnn_frontend) as follows:
+
+```bash
+# install cudnn
+pip install nvidia-cudnn-frontend
+```
+
+- Finally, install Thunder as follows:
 
 ```
 # install thunder

thunder/__init__.py

@@ -110,6 +110,7 @@
     # TODO Extend this
     # TODO Add device aliases
     # TODO Add executor aliases
+    "cudnn_executor",
     "sdpa_executor",
     "nvfuser_executor",
     "pytorch_executor",
@@ -163,17 +164,22 @@ def __version__():
 get_default_executors = extend.get_default_executors
 get_always_executors = extend.get_always_executors
 
+cudnn_executor: None | extend.Executor = extend.get_executor("cudnn")
 sdpa_executor: None | extend.Executor = extend.get_executor("sdpa")
 nvfuser_executor: None | extend.Executor = extend.get_executor("nvfuser")
 pytorch_executor: None | extend.Executor = extend.get_executor("torch")
 
-# Default executor list is [sdpa -> nvfuser -> torch -> python]
+# Default executor list is [cudnn -> sdpa -> nvfuser -> torch -> python]
+# Note that add_default_executor inserts executor at start of list, hence the reverse order below.
 if nvfuser_executor:
     add_default_executor(nvfuser_executor)
 
 if sdpa_executor:
     add_default_executor(sdpa_executor)
 
+if cudnn_executor:
+    add_default_executor(cudnn_executor)
+
 #
 # Promoted debugging functions
 #

thunder/executors/cudnnex.py

@@ -5,16 +5,36 @@
 import random
 
 from lightning_utilities.core.imports import package_available
+from looseversion import LooseVersion
 
 
-def cudnn_available() -> bool:
-    return package_available("cudnn")
+#
+# Functions for detecting cudnn and its version
+#
+def cudnn_version() -> LooseVersion | None:
+    try:
+        import cudnn
+
+        if hasattr(cudnn, "__version__"):
+            return LooseVersion(cudnn.__version__)
+
+        # NOTE: This import of cudnn may or may not have version info
+        return LooseVersion("0.0.0")
+    except ImportError:
+        pass
+
+    # NOTE This occurs when cudnn couldn't be imported
+    return None
 
 
-def cudnn_version() -> int:
-    if cudnn_available():
-        return cudnn.backend_version()
-    return 0
+def required_cudnn_version() -> LooseVersion:
+    # Using 1.3.0 majorly because it works better with other libraries (e.g. torch) that also build on top of cudnn backend
+    return LooseVersion("1.3.0")
+
+
+def cudnn_available() -> bool:
+    v = cudnn_version()
+    return v is not None and v >= required_cudnn_version()
 
 
 cudnn: None | Any = None
@@ -354,6 +374,9 @@ def _cudnn_sdpa_checker(
     if cudnn is None:
         return False
 
+    if query.device.type != "cuda" or key.device != query.device or value.device != query.device:
+        return False
+
     if len(query.size()) != 4:
         return False
     _, _, _, d_q = query.size()
@@ -367,6 +390,32 @@ def _cudnn_sdpa_checker(
         if d % 8 != 0 or d > 128:
             return False
 
+    try:
+        # Build both forward and backward graphs
+        query_4d, key_4d, value_4d, attn_mask_4d = _transform_sdpa_inputs(query, key, value, attn_mask)
+        _make_cudnn_sdpa_forward_graph(query_4d, key_4d, value_4d, attn_mask_4d, dropout_p, is_causal)
+        _make_cudnn_sdpa_backward_graph(
+            query_4d,
+            key_4d,
+            value_4d,
+            attn_mask_4d,
+            dropout_p,
+            is_causal,
+            query_4d.stride,
+            key_4d.stride,
+            value_4d.stride,
+        )
+    # If cudnn can't support the graph, return false
+    # Please turn on cudnn API logging for helpful messages that mention why the graph is not supported.
+    # For cudnn backend logging, refer https://docs.nvidia.com/deeplearning/cudnn/latest/reference/troubleshooting.html
+    # For cudnn frontend logging, refer https://gitlab-master.nvidia.com/cudnn/cudnn_frontend#debugging
+    except cudnn.cudnnGraphNotSupportedError as ex:
+        return False
+    # Otherwise just raise the error.
+    # These errors can be due to internal cudnn bugs, or user error.
+    except Exception as e:
+        raise
+
     return True
 
 
@@ -383,9 +432,6 @@ def _make_cudnn_sdpa_backward_graph(
     b, h, s_q, _ = query.size
     _, _, _, d_v = value.size
 
-    # cuDNN < 9.0.0 might produce nan gradients for sequence length < 64
-    assert s_q >= 64, "CUDNN SDPA requires sequence length to be at least 64 for backward pass"
-
     graph = cudnn.pygraph(
         io_data_type=torch_to_cudnn_dtype(query.dtype),
         intermediate_data_type=cudnn.data_type.FLOAT,

thunder/tests/test_cudnn_executor.py

@@ -18,7 +18,7 @@
 
 cudnn = pytest.importorskip("cudnn")
 from thunder.executors.cudnn_layernormex import cudnn_layernorm_ex
-from thunder.executors.cudnnex import cudnn_ex, cudnn_version
+from thunder.executors.cudnnex import cudnn_ex
 
 
 def _maybe_xfail() -> None:
@@ -196,7 +196,10 @@ def test_cudnn_vs_torch_consistency(op, device, dtype, *_):
             return result
 
 
-@pytest.mark.skipif(cudnn_version() < 8905, reason="cuDNN is required to be at least `8.9.5`")
+@pytest.mark.skipif(
+    LooseVersion(cudnn.backend_version_string()) < LooseVersion("8.9.5"),
+    reason="cuDNN is required to be at least `8.9.5`",
+)
 @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):