A FusionDefinition wrapper that takes/produces DTensors.

tests/python/test_dtensor.py

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+# SPDX-FileCopyrightText: Copyright (c) 2025-present NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+
+import multidevice_fixtures
+import nvfuser
+import pytest
+import torch
+import torch.distributed as dist
+from collections.abc import Iterable
+from nvfuser import DataType, FusionDefinition
+from torch.distributed.tensor import DTensor
+from torch.distributed.tensor.placement_types import Placement, Shard, Replicate
+from typing import Callable, cast
+
+
+multidevice_test = multidevice_fixtures.multidevice_test
+
+
+@pytest.fixture(scope="module")
+def setup_process_group(multidevice_test):
+    # The default port as used by https://github.com/pytorch/pytorch/blob/45a8b5682eb69d865cbf68c7f2f689b56b4efd53/torch/csrc/distributed/c10d/TCPStore.hpp#L51.
+    dist.init_process_group(
+        backend="nccl",
+        init_method="tcp://localhost:29500",
+        world_size=multidevice_test.size,
+        rank=multidevice_test.rank,
+    )
+    yield
+    dist.destroy_process_group()
+
+
+class FusionDefinitionWrapper:
+    def __init__(self, define_fusion: Callable[[FusionDefinition], None]):
+        """Wraps a function that defines a fusion without `multidevice_schedule`."""
+        self._define_fusion = define_fusion
+
+    def __call__(self, in_dtensors: Iterable[DTensor]) -> list[DTensor]:
+        define_fn = self._define_fusion
+
+        class Model(FusionDefinition):
+            def definition(self):
+                define_fn(self)
+
+            def _find_tensor_by_index(self, index: int) -> nvfuser.Tensor:
+                for t in self.sched.tensors():
+                    if t.index == index:
+                        return t
+                return None
+
+            def multidevice_schedule(self):
+                for in_tensor_index, in_dtensor in zip(self.inputs(), in_dtensors):
+                    in_tensor = self._find_tensor_by_index(in_tensor_index)
+
+                    # Set the device mesh.
+                    assert (
+                        in_dtensor.device_mesh.ndim == 1
+                    ), "nvFuser's Python API only supports 1D meshes."
+                    mesh = nvfuser.DeviceMesh(
+                        in_dtensor.device_mesh.mesh.view(-1).tolist()
+                    )
+                    self.sched._set_device_mesh(in_tensor, mesh)
+
+                    # Parallelize.
+                    assert len(in_dtensor.placements) == 1, "Expect a 1D mesh"
+                    placement: Placement = in_dtensor.placements[0]
+                    if placement.is_shard():
+                        dim = cast(Shard, placement).dim
+                        self.sched.parallelize(
+                            in_tensor, dim, nvfuser.ParallelType.mesh_x
+                        )
+
+        in_tensors = [in_dtensor.to_local() for in_dtensor in in_dtensors]
+        model = Model()
+        out_tensors = model.execute(in_tensors)
+
+        for i, out_tensor in enumerate(out_tensors):
+            if isinstance(out_tensor, nvfuser.DistributedTensor):
+                mesh = dist.device_mesh.init_device_mesh("cuda", [out_tensor.mesh.size])
+                placements: list[Placement] = []
+                for parallel_type in [nvfuser.ParallelType.mesh_x]:
+                    axis: int = out_tensor.axis_sharded_on(parallel_type)
+                    placements.append(Replicate() if axis == -1 else Shard(axis))
+                out_tensors[i] = DTensor.from_local(out_tensor.local, mesh, placements)
+        return out_tensors
+
+
+@pytest.mark.mpi
+def test_plus_one(setup_process_group):
+    def define_fusion(fd: FusionDefinition):
+        inp = fd.define_tensor(
+            (-1, -1), contiguity=(False, False), dtype=DataType.Float
+        )
+        one = fd.define_scalar(1.0, dtype=DataType.Float)
+        out = fd.ops.add(inp, one)
+        fd.add_output(out)
+
+    op = FusionDefinitionWrapper(define_fusion)
+
+    num_devices = dist.get_world_size()
+    rank = dist.get_rank()
+    torch.cuda.set_device(rank)
+
+    in_tensor = torch.randn(num_devices, 4)
+    mesh = dist.device_mesh.init_device_mesh("cuda", [num_devices])
+    in_dtensor = dist.tensor.distribute_tensor(in_tensor, mesh, [Shard(0)])
+
+    (out_dtensor,) = op([in_dtensor])
+    torch.testing.assert_close(out_dtensor.to_local(), in_dtensor.to_local() + 1)
+    assert out_dtensor.device_mesh == in_dtensor.device_mesh
+    assert out_dtensor.placements == in_dtensor.placements