Nvfuser python API import fix

CMakeLists.txt

@@ -1163,7 +1163,11 @@ if(NOT USE_CUDA AND NOT USE_ROCM)
 endif()
 
 if(BUILD_NVFUSER)
-  add_subdirectory(third_party/nvfuser)
+  if(DEFINED ENV{NVFUSER_SOURCE_DIR})
+    add_subdirectory($ENV{NVFUSER_SOURCE_DIR} nvfuser)
+  else()
+    add_subdirectory(third_party/nvfuser nvfuser)
+  endif()
 endif()
 
 include(cmake/Summary.cmake)

setup.py

@@ -175,6 +175,9 @@
 #   NCCL_INCLUDE_DIR
 #     specify where nccl is installed
 #
+#   NVFUSER_SOURCE_DIR
+#     specify nvfuser root directory
+#
 #   NVTOOLSEXT_PATH (Windows only)
 #     specify where nvtoolsext is installed
 #

test/test_prims.py

@@ -145,7 +145,11 @@ def test_nvfuser_impl_is_used(self, device):
         # This test is to ensure that when the nvfuser implementation exists it is used
         # Assuming one-to-one mapping between prims and nvfuser implementations
         # This test is not intended to test the correctness of the nvfuser implementation
-        from nvfuser._C import FusionDefinition as fd
+        try:
+            from nvfuser import FusionDefinition as fd
+        except ImportError:
+            from nvfuser._C import FusionDefinition as fd
+
 
         prim_nvfuser_ops = set(torch._prims.__all__).intersection(dir(fd.ops))
         ops_without_nvfuser_impl = {

torch/_prims/nvfuser_executor.py

@@ -19,12 +19,29 @@
 from torch.utils._pytree import tree_flatten, tree_map, tree_unflatten
 
 if torch.cuda.is_available():
-    from nvfuser._C import (  # type: ignore[import]
-        DataType,
-        Fusion,
-        FusionDefinition,
-        Tensor,
-    )
+    try:
+        from nvfuser import (  # type: ignore[attr-defined, import]
+            DataType,
+            FusionDefinition,
+            Tensor,
+        )
+
+        def create_fusion_definition():
+            fd = FusionDefinition()
+            return fd, fd
+
+    except ImportError:
+        from nvfuser._C import (  # type: ignore[import]
+            DataType,
+            Fusion,
+            FusionDefinition,
+            Tensor,
+        )
+
+        def create_fusion_definition():
+            fusion = Fusion()
+            return fusion, FusionDefinition(fusion)
+
 else:
     DataType = None
 
@@ -74,7 +91,10 @@ def compute_contiguity(shape, strides):
     Contiguous dimensions are represented by True, strided dimensions
     are represented by False.
     """
-    from nvfuser._C import compute_contiguity
+    try:
+        from nvfuser import compute_contiguity  # type: ignore[attr-defined]
+    except ImportError:
+        from nvfuser._C import compute_contiguity
 
     return compute_contiguity(shape, strides)
 
@@ -148,8 +168,8 @@ def make_nvfuser_fusion(gm: GraphModule, *nv_args_templates):
     output_node = next(filter(lambda n: n.op == "output", gm.graph.nodes))
     orig_flat_out, _ = tree_flatten(output_node.args[0])
 
-    fusion = Fusion()
-    with FusionDefinition(fusion) as fd:
+    fusion, fd = create_fusion_definition()
+    with fd:
 
         def _to_nvfuser_constant(arg):
             if isinstance(arg, Number):

torch/_prims/nvfuser_prims.py

@@ -143,7 +143,12 @@
 
 def _assert_nvfuser_op_exists(fname: str):
     try:
-        from nvfuser._C import FusionDefinition as fd  # type: ignore[import]
+        try:
+            from nvfuser import (  # type: ignore[import, attr-defined]
+                FusionDefinition as fd,
+            )
+        except ImportError:
+            from nvfuser._C import FusionDefinition as fd  # type: ignore[import]
 
         assert getattr(fd.Operators, fname)
     except ImportError:
@@ -285,7 +290,10 @@ def _sum_nvfuser(
     dims: DimsSequenceType,
 ):
     keep_dims = False
-    from nvfuser._C import DataType  # type: ignore[import]
+    try:
+        from nvfuser import DataType  # type: ignore[import, attr-defined]
+    except ImportError:
+        from nvfuser._C import DataType  # type: ignore[import]
 
     output_dtype = DataType.Null
     return fd.ops.sum(a, dims, keep_dims, output_dtype)

torch/_prims_common/__init__.py

@@ -9,7 +9,10 @@
 from torch import sym_float, sym_int, sym_max
 
 try:
-    from nvfuser._C import DataType  # type: ignore[import]
+    try:
+        from nvfuser import DataType  # type: ignore[import, attr-defined]
+    except ImportError:
+        from nvfuser._C import DataType  # type: ignore[import]
 
     _torch_dtype_to_nvfuser_dtype_map = {
         torch.cdouble: DataType.ComplexDouble,