cuda partioner supported (#14477)

Summary:

This diff introduce partitioner for cuda delegated, which driven by aoti library. 

The partitioner will partition the input into exactly one partitioned graph that contains all operators from the input graph, also will keep all operators (except the ops that couldn't handled by aoti-cuda lib) away from executorch operator decomposition. Operator will be decomposed in the cuda backend using aoti-cuda specific decomposition table.

Reviewed By: larryliu0820

Differential Revision: D82987193

Author: Gasoonjia

backends/cuda/TARGETS

@@ -0,0 +1,18 @@
+load("@fbsource//xplat/executorch/build:runtime_wrapper.bzl", "runtime")
+
+oncall("executorch")
+
+runtime.python_library(
+    name = "cuda_partitioner",
+    srcs = [
+        "cuda_partitioner.py",
+    ],
+    visibility = [
+        "//executorch/...",
+    ],
+    deps = [
+        "//caffe2:torch",
+        "//executorch/exir/backend:partitioner",
+        "//executorch/exir/backend:utils",
+    ],
+)

backends/cuda/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.

backends/cuda/cuda_partitioner.py

@@ -0,0 +1,69 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Callable, Dict, final, List, Optional, Tuple
+
+import torch
+from executorch.exir.backend.compile_spec_schema import CompileSpec
+from executorch.exir.backend.partitioner import (
+    DelegationSpec,
+    Partitioner,
+    PartitionResult,
+)
+from executorch.exir.backend.utils import tag_constant_data
+from torch.export.exported_program import ExportedProgram
+
+
+@final
+class CudaPartitioner(Partitioner):
+    """
+    CUDA partitioner for AOTInductor backend integration.
+
+    This partitioner creates a single partition containing all operators from the input graph.
+    It skips core ATen decomposition, allowing the CUDA backend to handle decomposition using
+    AOTInductor's CUDA-specific decomposition table.
+
+    Only operators that cannot be handled by the aoti-cuda library will be excluded from
+    the partition and fall back to ExecuTorch's default or custom handling.
+    """
+
+    def __init__(self, compile_spec: List[CompileSpec]) -> None:
+        self.delegation_spec = DelegationSpec("CudaBackend", compile_spec)
+
+    def partition(self, exported_program: ExportedProgram) -> PartitionResult:
+        """
+        Fully delegate the graph to AOTInductor by tagging all nodes as a single partition.
+        """
+
+        partition_tags: Dict[str, DelegationSpec] = {}
+        for node in exported_program.graph.nodes:
+            if node.op != "call_function":
+                continue
+            tag = "tag0"
+            node.meta["delegation_tag"] = tag
+            partition_tags[tag] = self.delegation_spec
+
+        tag_constant_data(exported_program)
+
+        return PartitionResult(
+            tagged_exported_program=exported_program, partition_tags=partition_tags
+        )
+
+    def ops_to_not_decompose(
+        self, ep: ExportedProgram
+    ) -> Tuple[List[torch._ops.OpOverload], Optional[Callable[[torch.fx.Node], bool]]]:
+        """
+        Return a list of operations that should not be decomposed and let the AOT compiler handle them.
+        Currently we skip ATen decompositon for all ops, and let the cuda backend handle them.
+        """
+        do_not_decompose = set()
+
+        for node in ep.graph.nodes:
+            if node.op == "call_function" and isinstance(
+                node.target, torch._ops.OpOverload
+            ):
+                do_not_decompose.add(node.target)
+        return list(do_not_decompose), None

backends/cuda/tests/TARGETS

@@ -0,0 +1,20 @@
+load("@fbsource//xplat/executorch/build:runtime_wrapper.bzl", "runtime")
+load("@fbcode_macros//build_defs:python_unittest.bzl", "python_unittest")
+
+oncall("executorch")
+
+python_unittest(
+    name = "test_cuda_partitioner",
+    srcs = [
+        "test_cuda_partitioner.py",
+    ],
+    visibility = [
+        "//executorch/...",
+    ],
+    deps = [
+        "//caffe2:torch",
+        "//executorch/backends/cuda:cuda_partitioner",
+        "//executorch/exir:lib",
+        "//executorch/exir/backend:compile_spec_schema",
+    ],
+)

backends/cuda/tests/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.

backends/cuda/tests/test_cuda_partitioner.py

@@ -0,0 +1,149 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import unittest
+from typing import Tuple
+
+import torch
+from executorch.backends.cuda.cuda_partitioner import CudaPartitioner
+from executorch.exir.backend.compile_spec_schema import CompileSpec
+from executorch.exir.backend.partitioner import PartitionResult
+from torch.export import export
+
+
+class TestCudaPartitioner(unittest.TestCase):
+    """
+    Test CUDA partitioner functionality.
+
+    After CUDA partitioning, there should be exactly one partitioned graph that contains
+    all operators from the input graph. This means all operators should be tagged with
+    the same delegation tag, indicating they will all be executed by the CUDA backend.
+    """
+
+    def setUp(self):
+        """Set up test environment."""
+        # Skip tests if CUDA is not available
+        if not torch.cuda.is_available():
+            self.skipTest("CUDA is not available")
+
+    def _get_partition_result(
+        self, module: torch.nn.Module, inputs: Tuple[torch.Tensor, ...]
+    ) -> PartitionResult:
+        """Helper method to get partition result for a given module."""
+        # Export the model
+        exported_program = export(module, inputs, strict=True)
+
+        # Create partitioner and compile specs
+        compile_specs = [CompileSpec("cuda_compile_options", b"")]
+        partitioner = CudaPartitioner(compile_specs)
+
+        # Get partition result
+        partition_result = partitioner.partition(exported_program)
+
+        # Verify partition result structure
+        self.assertIsNotNone(partition_result)
+        self.assertTrue(hasattr(partition_result, "tagged_exported_program"))
+        self.assertTrue(hasattr(partition_result, "partition_tags"))
+
+        return partition_result
+
+    def _check_fully_partitioned(self, partition_result: PartitionResult) -> bool:
+        """Check if the graph is fully partitioned (all operators have the same tag)."""
+        tagged_nodes = []
+        untagged_ops = []
+
+        for node in partition_result.tagged_exported_program.graph.nodes:
+            if node.op == "call_function":
+                if hasattr(node, "meta") and "delegation_tag" in node.meta:
+                    tagged_nodes.append(node)
+                else:
+                    untagged_ops.append(node)
+
+        # Check if we have any tagged nodes
+        if not tagged_nodes:
+            return False
+
+        # Check if all tagged nodes have the same tag
+        first_tag = tagged_nodes[0].meta["delegation_tag"]
+        all_same_tag = all(
+            node.meta.get("delegation_tag") == first_tag for node in tagged_nodes
+        )
+
+        # Should have no untagged operations for full partitioning
+        fully_partitioned = len(untagged_ops) == 0 and all_same_tag
+
+        return fully_partitioned
+
+    def test_simple_add_partition(self):
+        """
+        Test that CUDA partitioner creates exactly one partition containing all operators.
+        Simple element-wise addition should result in a single graph with all ops tagged identically.
+        """
+
+        class AddModule(torch.nn.Module):
+            def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+                return x + y
+
+        module = AddModule()
+        inputs = (torch.randn(3, 4), torch.randn(3, 4))
+
+        partition_result = self._get_partition_result(module, inputs)
+        fully_partitioned = self._check_fully_partitioned(partition_result)
+
+        self.assertTrue(
+            fully_partitioned,
+            "Graph should be fully partitioned with all operators having the same tag",
+        )
+
+    def test_conv2d_partition(self):
+        """
+        Test that CUDA partitioner creates exactly one partition containing all operators.
+        Conv2D operation should result in a single graph with all ops tagged identically.
+        """
+
+        class Conv2dModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.conv = torch.nn.Conv2d(3, 16, kernel_size=3, padding=1)
+
+            def forward(self, x: torch.Tensor) -> torch.Tensor:
+                return self.conv(x)
+
+        module = Conv2dModule()
+        inputs = (torch.randn(1, 3, 32, 32),)
+
+        partition_result = self._get_partition_result(module, inputs)
+        fully_partitioned = self._check_fully_partitioned(partition_result)
+
+        self.assertTrue(
+            fully_partitioned,
+            "Graph should be fully partitioned with all operators having the same tag",
+        )
+
+    def test_linear_partition(self):
+        """
+        Test that CUDA partitioner creates exactly one partition containing all operators.
+        Linear layer operation should result in a single graph with all ops tagged identically.
+        """
+
+        class LinearModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.linear = torch.nn.Linear(128, 64)
+
+            def forward(self, x: torch.Tensor) -> torch.Tensor:
+                return self.linear(x)
+
+        module = LinearModule()
+        inputs = (torch.randn(8, 128),)
+
+        partition_result = self._get_partition_result(module, inputs)
+        fully_partitioned = self._check_fully_partitioned(partition_result)
+
+        self.assertTrue(
+            fully_partitioned,
+            "Graph should be fully partitioned with all operators having the same tag",
+        )