Merge branch 'main' into export-D87519599

Author: Gasoonjia

backends/aoti/aoti_backend.py

@@ -70,10 +70,15 @@ def get_aoti_compile_options(
 
     @classmethod
     @abstractmethod
-    def get_custom_passes(cls) -> List[typing.Any]:
+    def get_custom_passes(cls, compile_specs: List[CompileSpec]) -> List[typing.Any]:
         """Return the list of custom passes to apply after ReplaceViewCopyWithViewPass and before decomposition."""
         pass
 
+    @classmethod
+    def get_extra_aoti_compile_context_manager(cls):
+        """Return extra context manager to apply during aoti_compile stage. By default returns an empty context manager."""
+        return contextlib.nullcontext()
+
     @classmethod
     @contextlib.contextmanager
     def collect_unsupported_fallback_kernels(cls, missing_fallback_kernels: Set[str]):
@@ -149,7 +154,7 @@ def preprocess(
         ReplaceViewCopyWithViewPass()(device_edge_program.graph_module)
 
         # Apply custom backend-specific passes
-        custom_passes = cls.get_custom_passes()
+        custom_passes = cls.get_custom_passes(compile_specs)
         for custom_pass in custom_passes:
             custom_pass(device_edge_program.graph_module)
 
@@ -174,7 +179,7 @@ def preprocess(
         # Compile with fallback kernel collection
         with cls.collect_unsupported_fallback_kernels(
             missing_fallback_kernels
-        ), torch.no_grad():
+        ), torch.no_grad(), cls.get_extra_aoti_compile_context_manager():
             paths = torch._inductor.aot_compile(
                 edge_program_module, tuple(user_input_placeholders), options=options
             )

backends/apple/metal/metal_backend.py

@@ -42,7 +42,7 @@ def get_decomposition_table(cls) -> Dict[Any, Any]:
         return {}
 
     @classmethod
-    def get_custom_passes(cls) -> List[typing.Any]:
+    def get_custom_passes(cls, compile_specs: List[CompileSpec]) -> List[typing.Any]:
         """Return Metal-specific passes (currently none)"""
         return []
 

backends/cuda/cuda_backend.py

@@ -17,6 +17,7 @@
 from executorch.exir.backend.backend_details import BackendDetails
 from executorch.exir.backend.compile_spec_schema import CompileSpec
 from torch._inductor.decomposition import conv1d_to_conv2d
+from torch.nn.attention import SDPBackend
 
 
 @final
@@ -47,9 +48,27 @@ def get_decomposition_table(cls) -> Dict[Any, Any]:
         }
 
     @classmethod
-    def get_custom_passes(cls) -> List[typing.Any]:
-        """Return CUDA-specific passes: ReplaceEdgeOpWithTritonOpPass"""
-        return [ReplaceEdgeOpWithTritonOpPass()]
+    def get_custom_passes(cls, compile_specs: List[CompileSpec]) -> List[typing.Any]:
+        """
+        Return CUDA-specific passes: ReplaceEdgeOpWithTritonOpPass.
+
+        The Triton kernel replacement behavior can be controlled via compile_specs:
+        - triton_kernel_mode="ON": Always use Triton kernels
+        - triton_kernel_mode="OFF": Never use Triton kernels and fallback to other implementations like cuda or decomposed operator.
+        """
+        # Parse compile_specs for triton_kernel_mode
+        triton_kernel_mode = "ON"  # Default mode
+        for spec in compile_specs:
+            if spec.key == "triton_kernel_mode":
+                mode = spec.value.decode("utf-8").upper()
+                if mode not in ["ON", "OFF"]:
+                    raise ValueError(
+                        f"Invalid triton_kernel_mode: {mode}. "
+                        f"Expected 'ON' or 'OFF'."
+                    )
+                triton_kernel_mode = mode
+
+        return [ReplaceEdgeOpWithTritonOpPass()] if triton_kernel_mode == "ON" else []
 
     @classmethod
     def get_aoti_compile_options(
@@ -114,3 +133,21 @@ def get_aoti_compile_options(
             ), "shim_library_path should not be set for Linux"
 
         return options
+
+    @classmethod
+    def get_extra_aoti_compile_context_manager(cls):
+        """
+        Return SDPA MATH backend context manager for CUDA compilation.
+
+        This context manager plays as a fallback solution for any remaining PyTorch SDPA
+        operations to use the MATH backend (decomposed SDPA) during AOTInductor compilation.
+
+        Note:
+        - If SDPA ops are replaced with Triton kernels by ReplaceEdgeOpWithTritonOpPass,
+          this context manager will have no effect on those ops (they are no longer
+          PyTorch SDPA ops).
+        - If SDPA ops are NOT replaced (e.g., when triton_kernel_mode="OFF"), this
+          context manager will force them to use the MATH backend, causing them to
+          be automatically decomposed during compilation.
+        """
+        return torch.nn.attention.sdpa_kernel([SDPBackend.MATH])

backends/cuda/tests/test_cuda_export.py

@@ -12,6 +12,7 @@
 from executorch.backends.cuda.cuda_partitioner import CudaPartitioner
 from executorch.examples.models.toy_model import SdpaModule
 from executorch.exir import EdgeCompileConfig, to_edge_transform_and_lower
+from executorch.exir.backend.compile_spec_schema import CompileSpec
 from torch.export import export
 
 
@@ -25,16 +26,27 @@ def setUp(self):
             self.skipTest("CUDA is not available")
 
     def _export_to_cuda_with_lower(
-        self, module: torch.nn.Module, inputs: Tuple[torch.Tensor, ...]
+        self,
+        module: torch.nn.Module,
+        inputs: Tuple[torch.Tensor, ...],
+        compile_specs: list[CompileSpec] | None = None,
     ) -> None:
-        """Helper method to export a module to CUDA backend using to_edge_transform_and_lower."""
+        """Helper method to export a module to CUDA backend using to_edge_transform_and_lower.
+
+        Args:
+            module: The torch.nn.Module to export
+            inputs: The example inputs for the module
+            compile_specs: Optional list of compile specs. If not provided, defaults to
+                          only the method name compile spec for "forward"
+        """
         # Export the model
         exported_program = export(module, inputs, strict=True)
 
-        # Create partitioner and compile specs
-        partitioner = CudaPartitioner(
-            [CudaBackend.generate_method_name_compile_spec("forward")]
-        )
+        # Create partitioner with compile specs
+        if compile_specs is None:
+            compile_specs = [CudaBackend.generate_method_name_compile_spec("forward")]
+
+        partitioner = CudaPartitioner(compile_specs)
 
         # Use to_edge_transform_and_lower for complete pipeline
         edge_program_manager = to_edge_transform_and_lower(
@@ -288,3 +300,28 @@ def test_sdpa_single_kernel(self):
             edge_program_manager,
             "SDPA single kernel operation export failed",
         )
+
+    def test_triton_kernel_mode_off(self):
+        """
+        Test CUDA export with triton_kernel_mode set to OFF for SDPA kernel.
+        This validates that the backend correctly processes the triton_kernel_mode
+        compile spec and can export SDPA operations without Triton kernel replacements.
+        When triton_kernel_mode is OFF, SDPA should be decomposed using the MATH backend.
+        """
+
+        sdpa = SdpaModule()
+
+        # Create compile specs with triton_kernel_mode set to OFF
+        compile_specs = [
+            CudaBackend.generate_method_name_compile_spec("forward"),
+            CompileSpec(key="triton_kernel_mode", value=b"OFF"),
+        ]
+
+        # Test export with triton_kernel_mode=OFF
+        edge_program_manager = self._export_to_cuda_with_lower(
+            sdpa.get_eager_model(), sdpa.get_example_inputs(), compile_specs
+        )
+        self.assertIsNotNone(
+            edge_program_manager,
+            "SDPA kernel export with triton_kernel_mode=OFF failed",
+        )

docs/source/backends/xnnpack/xnnpack-troubleshooting.md

@@ -23,3 +23,27 @@ The XNNPACK backend is built by default for Python, Android, iOS, and in most CM
  * Use [op-level profiling](/tutorials/devtools-integration-tutorial) to understand which operators are taking the most time. <!-- @lint-ignore linter doesn't like this link for some reason -->
    * The XNNPACK backend provides operator-level timing for delegated operators.
  * See general performance troubleshooting tips in [Performance Troubleshooting](/using-executorch-faqs.md#inference-is-slow-performance-troubleshooting).
+
+
+## Debugging Why Nodes Are Not Partitioned
+
+* To debug cases where operators are not delegated to XNNPACK,
+you can enable internal debug logs before **to_edge_transform_and_lower** from the partitioner. This will print diagnostic messages explaining why specific nodes fail
+to partition.
+
+``` python
+
+# caption: Enable internal partition debug logging
+
+import logging
+
+logger = logging.getLogger("executorch.backends.xnnpack.partition")
+logger.setLevel(logging.DEBUG)
+
+if not logger.handlers:
+    ch = logging.StreamHandler()
+    ch.setLevel(logging.DEBUG)
+    formatter = logging.Formatter("[%(levelname)s] %(name)s: %(message)s")
+    ch.setFormatter(formatter)
+    logger.addHandler(ch)
+```