Attempt to DID-parallelize a loop split in Python.

csrc/python_frontend/fusion_definition.cpp

@@ -314,6 +314,15 @@ void FusionDefinition::finalizeSchedule(
   // Users can access schedule objects after scheduling the fusion.
 }
 
+void FusionDefinition::setupMultideviceSchedule() {
+  prev_fusion_ = FusionGuard::getCurFusion();
+  FusionGuard::setCurFusion(preschedFusion());
+}
+
+void FusionDefinition::finalizeMultideviceSchedule() {
+  FusionGuard::setCurFusion(prev_fusion_);
+}
+
 void FusionDefinition::print(std::ostream& os) const {
   if (id().has_value()) {
     os << "\ndef nvfuser_fusion_id" << id().value();

csrc/python_frontend/fusion_definition.h

@@ -184,6 +184,8 @@ class NVF_API FusionDefinition : public FusionState {
   //! Finalized use scheduling of a fusion
   //! resets FusionGuard, lowers IR to a kernel, compiles kernel
   NVF_API void finalizeSchedule(const at::ArrayRef<c10::IValue>& inputs);
+  NVF_API void setupMultideviceSchedule();
+  NVF_API void finalizeMultideviceSchedule();
   //! Prints a python function representing the definition
   NVF_API void print(std::ostream& os) const;
   //! Executes a fusion if a valid definition or cache lookup occurred prior

csrc/python_frontend/python_bindings.cpp

@@ -1091,6 +1091,12 @@ void initNvFuserPythonBindings(PyObject* module) {
             // Mark the end of a schedule
             inst::Trace::instance()->endEvent(nullptr);
           })
+      .def(
+          "_setup_multidevice_schedule",
+          [](FusionDefinition& self) { self.setupMultideviceSchedule(); })
+      .def(
+          "_finalize_multidevice_schedule",
+          [](FusionDefinition& self) { self.finalizeMultideviceSchedule(); })
       .def("inputs", [](FusionDefinition& self) { return self.inputs(); })
       .def("outputs", [](FusionDefinition& self) { return self.outputs(); })
       .def("extents", [](FusionDefinition& self) { return self.extents(); })
@@ -3596,7 +3602,6 @@ void initNvFuserPythonBindings(PyObject* module) {
       },
       py::arg("tensor"),
       py::arg("mesh"));
-  //! experimental API for multidevice support
   nvf_sched.def(
       "parallelize",
       [](FusionDefinition::SchedOperators& self,
@@ -3683,6 +3688,18 @@ void initNvFuserPythonBindings(PyObject* module) {
       py::arg("dim"),
       py::arg("factor"),
       py::arg("inner_split") = true);
+  nvf_sched.def(
+      "set_allocation_as_loop",
+      [](FusionDefinition::SchedOperators& self, Tensor arg) {
+        FUSER_PERF_SCOPE("SchedOperators.set_allocation_as_loop");
+        NVF_CHECK(
+            self.validUse(),
+            "Attempting to use a SchedOperators Op prior to definition!");
+        FusionDefinition* fd = self.fusion_definition;
+        auto* tv = fd->getFusionState(arg.index)->template as<TensorView>();
+        tv->setAllocationDomain(tv->getLoopDomain(), true);
+      },
+      py::arg("arg"));
   nvf_sched.def(
       "cache_after",
       [](FusionDefinition::SchedOperators& self,

nvfuser/__init__.py

@@ -284,7 +284,9 @@ def execute(
             #
             # Note: there's a plan to embed multidevice schedules into FusionDefinition
             # as annotating nodes. This may eventually replace `multidevice_schedule`.
+            self._setup_multidevice_schedule()
             self.multidevice_schedule()
+            self._finalize_multidevice_schedule()
 
         # If schedule is defined by child class and schedule is not defined for
         # inputs, make a schedule.

tests/python/mpi_fixtures.py

@@ -38,6 +38,10 @@ def local_rank(self):
     def barrier(self):
         self._communicator.barrier()
 
+    def shard_tensor(self, t: torch.Tensor, dim: int) -> torch.Tensor:
+        assert t.is_cpu
+        return t.tensor_split(self.size, dim)[self.rank].cuda(self.local_rank)
+
 
 @pytest.fixture(scope="session")
 def mpi_test():

tests/python/test_communication.py

@@ -0,0 +1,46 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-present NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+
+import pytest
+import torch
+
+import mpi_fixtures
+import nvfuser
+from nvfuser import DataType, FusionDefinition
+
+
+mpi_test = mpi_fixtures.mpi_test
+
+
+@pytest.mark.mpi
+def test_allgather(mpi_test):
+    num_devices = mpi_test.size
+    rank = mpi_test.rank
+
+    unsharded = torch.randn(num_devices * 4)
+    sharded = mpi_test.shard_tensor(unsharded, 0)
+
+    class Model(FusionDefinition):
+        def definition(self):
+            self.inp = self.define_tensor(
+                (num_devices * 4,), contiguity=True, dtype=DataType.Float
+            )
+            self.out = self.ops.set(self.inp)
+            self.add_output(self.out)
+
+        def multidevice_schedule(self):
+            mesh = self.sched._create_device_mesh(range(num_devices))
+            self.sched._set_device_mesh(self.inp, mesh)
+            self.sched._set_device_mesh(self.out, mesh)
+
+            self.sched.split(self.inp, 0, num_devices, False)
+            self.sched.parallelize(self.inp, 0, nvfuser.ParallelType.mesh_x)
+            self.sched.set_allocation_as_loop(self.inp)
+
+            self.sched.split(self.out, 0, num_devices, False)
+            self.sched.set_allocation_as_loop(self.out)
+
+    fd = Model()
+    outputs = fd.execute([sharded])
+    torch.testing.assert_close(outputs[0].cpu(), unsharded)

tests/python/test_multidevice.py

@@ -35,10 +35,8 @@ def test_pointwise(mpi_test):
     num_devices = mpi_test.size
     rank = mpi_test.rank
 
-    torch.cuda.set_device(mpi_test.local_rank)
-
-    unsharded_input = torch.randn(num_devices, 4, device="cuda")
-    sharded_input = unsharded_input[rank : rank + 1]
+    unsharded_input = torch.randn(num_devices, 4)
+    sharded_input = mpi_test.shard_tensor(unsharded_input, 0)
 
     class Model(FusionDefinition):
         def definition(self):
@@ -58,7 +56,7 @@ def multidevice_schedule(self):
 
     fd = Model()
     outputs = fd.execute([sharded_input])
-    torch.testing.assert_close(outputs[0], unsharded_input.relu() * 2)
+    torch.testing.assert_close(outputs[0].cpu(), unsharded_input.relu() * 2)
 
 
 @pytest.mark.mpi