Add scuba logging to edge API's (#7103)

Summary:
Adding scuba logging to the Edge AOT API's. Whenever the API's are used this will log some basic details to Scuba and also if the API call fails it will log the stack trace and the reason why it failed, to Scuba.

This is only enabled for internal users and for OSS users it'll essentially be a no-op.

The cases where logging is done:
- User uses the API in a notebook
- User uses the API via `buck run` or `buck test` on devserver

Logging will not be done if the API is called via automated tests.


Reviewed By: JacobSzwejbka

Differential Revision: D66385141

Author: tarun292

exir/emit/test/test_emit.py

@@ -340,9 +340,9 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         exir.print_program.pretty_print(program)
 
         deboxed_int_list = []
-        for item in program.execution_plan[0].values[5].val.items:  # pyre-ignore[16]
+        for item in program.execution_plan[0].values[5].val.items:
             deboxed_int_list.append(
-                program.execution_plan[0].values[item].val.int_val  # pyre-ignore[16]
+                program.execution_plan[0].values[item].val.int_val
             )
 
         self.assertEqual(IntList(deboxed_int_list), IntList([2, 0, 1]))
@@ -459,7 +459,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         # Check the mul operator's stack trace contains f -> g -> h
         self.assertTrue(
             "return torch.mul(x, torch.randn(3, 2))"
-            in program.execution_plan[0]  # pyre-ignore[16]
+            in program.execution_plan[0]
             .chains[0]
             .stacktrace[1]
             .items[-1]
@@ -618,7 +618,7 @@ def false_fn(y: torch.Tensor) -> torch.Tensor:
 
             op = (
                 program.execution_plan[0]
-                .operators[inst.instr_args.op_index]  # pyre-ignore[16]
+                .operators[inst.instr_args.op_index]
                 .name
             )
 
@@ -657,7 +657,7 @@ def map_fn(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
         # generate the tensor on which this iteration will operate on.
         self.assertEqual(
             op_table[
-                program.execution_plan[0]  # pyre-ignore[16]
+                program.execution_plan[0]
                 .chains[0]
                 .instructions[0]
                 .instr_args.op_index
@@ -666,7 +666,7 @@ def map_fn(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
         )
         self.assertEqual(
             op_table[
-                program.execution_plan[0]  # pyre-ignore[16]
+                program.execution_plan[0]
                 .chains[0]
                 .instructions[1]
                 .instr_args.op_index
@@ -681,7 +681,7 @@ def map_fn(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
         # We check here that both of these have been generated.
         self.assertEqual(
             op_table[
-                program.execution_plan[0]  # pyre-ignore[16]
+                program.execution_plan[0]
                 .chains[0]
                 .instructions[-5]
                 .instr_args.op_index
@@ -690,7 +690,7 @@ def map_fn(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
         )
         self.assertEqual(
             op_table[
-                program.execution_plan[0]  # pyre-ignore[16]
+                program.execution_plan[0]
                 .chains[0]
                 .instructions[-4]
                 .instr_args.op_index
@@ -699,7 +699,7 @@ def map_fn(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
         )
         self.assertEqual(
             op_table[
-                program.execution_plan[0]  # pyre-ignore[16]
+                program.execution_plan[0]
                 .chains[0]
                 .instructions[-3]
                 .instr_args.op_index
@@ -716,7 +716,7 @@ def map_fn(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
         )
         self.assertEqual(
             op_table[
-                program.execution_plan[0]  # pyre-ignore[16]
+                program.execution_plan[0]
                 .chains[0]
                 .instructions[-1]
                 .instr_args.op_index
@@ -1300,7 +1300,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         # this triggers the actual emission of the graph
         program = program_mul._emitter_output.program
         node = None
-        program.execution_plan[0].chains[0].instructions[  # pyre-ignore[16]
+        program.execution_plan[0].chains[0].instructions[
             0
         ].instr_args.op_index
 
@@ -1314,7 +1314,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         # Find the multiplication instruction in the program that was emitted.
         for idx in range(len(program.execution_plan[0].chains[0].instructions)):
             instruction = program.execution_plan[0].chains[0].instructions[idx]
-            op_index = instruction.instr_args.op_index  # pyre-ignore[16]
+            op_index = instruction.instr_args.op_index
             if "mul" in program.execution_plan[0].operators[op_index].name:
                 break
 
@@ -1454,7 +1454,7 @@ def forward(self, x, y):
         self.assertIsNotNone(exec_prog.delegate_map)
         self.assertIsNotNone(exec_prog.delegate_map.get("forward"))
         self.assertIsNotNone(
-            exec_prog.delegate_map.get("forward").get(0)  # pyre-ignore[16]
+            exec_prog.delegate_map.get("forward").get(0)
         )
         self.assertEqual(
             exec_prog.delegate_map.get("forward").get(0).get("name"),
@@ -1568,7 +1568,7 @@ def forward(self, x):
         model = model.to_executorch()
         model.dump_executorch_program(True)
         self.assertTrue(
-            model.executorch_program.execution_plan[0]  # pyre-ignore[16]
+            model.executorch_program.execution_plan[0]
             .values[0]
             .val.allocation_info
             is not None
@@ -1611,7 +1611,7 @@ def forward(self, x):
         )
         model.dump_executorch_program(True)
         self.assertTrue(
-            model.executorch_program.execution_plan[0]  # pyre-ignore[16]
+            model.executorch_program.execution_plan[0]
             .values[0]
             .val.allocation_info
             is not None

exir/program/TARGETS

@@ -1,4 +1,5 @@
 load("@fbcode_macros//build_defs:python_library.bzl", "python_library")
+load("@fbsource//xplat/executorch/build:runtime_wrapper.bzl", "runtime")
 
 oncall("executorch")
 
@@ -43,7 +44,7 @@ python_library(
         "//executorch/exir/passes:spec_prop_pass",
         "//executorch/exir/passes:weights_to_outputs_pass",
         "//executorch/exir/verification:verifier",
-    ],
+    ] +  (["//executorch/exir/program/fb:logger"] if not runtime.is_oss else [])
 )
 
 python_library(

exir/program/_program.py

@@ -75,8 +75,24 @@
 
 Val = Any
 
+from typing import Any, Callable
+
 from torch.library import Library
 
+try:
+    from executorch.exir.program.fb.logger import et_logger
+except ImportError:
+    # Define a stub decorator that does nothing
+    def et_logger(api_name: str) -> Callable[[Any], Any]:
+        def decorator(func: Callable[..., Any]) -> Callable[..., Any]:
+            def wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
+                return func(self, *args, **kwargs)
+
+            return wrapper
+
+        return decorator
+
+
 # This is the reserved namespace that is used to register ops to that will
 # be prevented from being decomposed during to_edge_transform_and_lower.
 edge_no_decomp_namespace = "EDGE_DO_NOT_DECOMP"
@@ -957,6 +973,7 @@ def _gen_edge_manager_for_partitioners(
     return edge_manager
 
 
+@et_logger("to_edge_transform_and_lower")
 def to_edge_transform_and_lower(
     programs: Union[ExportedProgram, Dict[str, ExportedProgram]],
     transform_passes: Optional[
@@ -1110,6 +1127,7 @@ def to_edge_with_preserved_ops(
     )
 
 
+@et_logger("to_edge")
 def to_edge(
     programs: Union[ExportedProgram, Dict[str, ExportedProgram]],
     constant_methods: Optional[Dict[str, Any]] = None,
@@ -1204,8 +1222,10 @@ def exported_program(self, method_name: str = "forward") -> ExportedProgram:
         """
         Returns the ExportedProgram specified by 'method_name'.
         """
+
         return self._edge_programs[method_name]
 
+    @et_logger("transform")
     def transform(
         self,
         passes: Union[Sequence[PassType], Dict[str, Sequence[PassType]]],
@@ -1253,6 +1273,7 @@ def transform(
             new_programs, copy.deepcopy(self._config_methods), compile_config
         )
 
+    @et_logger("to_backend")
     def to_backend(
         self, partitioner: Union[Partitioner, Dict[str, Partitioner]]
     ) -> "EdgeProgramManager":
@@ -1296,6 +1317,7 @@ def to_backend(
             new_edge_programs, copy.deepcopy(self._config_methods), config
         )
 
+    @et_logger("to_executorch")
     def to_executorch(
         self,
         config: Optional[ExecutorchBackendConfig] = None,

exir/tests/test_joint_graph.py

@@ -73,25 +73,25 @@ def forward(self, x, y):
 
         # assert that the weight and bias have proper data_buffer_idx and allocation_info
         self.assertEqual(
-            et.executorch_program.execution_plan[0]  # pyre-ignore
+            et.executorch_program.execution_plan[0]
             .values[0]
             .val.data_buffer_idx,
             1,
         )
         self.assertEqual(
-            et.executorch_program.execution_plan[0]  # pyre-ignore
+            et.executorch_program.execution_plan[0]
             .values[1]
             .val.data_buffer_idx,
             2,
         )
         self.assertEqual(
-            et.executorch_program.execution_plan[0]  # pyre-ignore
+            et.executorch_program.execution_plan[0]
             .values[0]
             .val.allocation_info.memory_offset_low,
             0,
         )
         self.assertEqual(
-            et.executorch_program.execution_plan[0]  # pyre-ignore
+            et.executorch_program.execution_plan[0]
             .values[1]
             .val.allocation_info.memory_offset_low,
             48,
@@ -106,7 +106,7 @@ def forward(self, x, y):
 
         self.assertTrue(torch.allclose(loss, et_outputs[0]))
         self.assertTrue(
-            torch.allclose(m.linear.weight.grad, et_outputs[1])  # pyre-ignore[6]
+            torch.allclose(m.linear.weight.grad, et_outputs[1])[6] # pyre-ignore
         )
         self.assertTrue(torch.allclose(m.linear.bias.grad, et_outputs[2]))
         self.assertTrue(torch.allclose(m.linear.weight, et_outputs[3]))
@@ -118,22 +118,22 @@ def forward(self, x, y):
 
         # gradient outputs start at index 1
         self.assertEqual(
-            et.executorch_program.execution_plan[1]  # pyre-ignore
+            et.executorch_program.execution_plan[1]
             .values[0]
             .val.int_val,
             1,
         )
 
         self.assertEqual(
-            et.executorch_program.execution_plan[2]  # pyre-ignore
+            et.executorch_program.execution_plan[2]
             .values[0]
             .val.string_val,
             "linear.weight",
         )
 
         # parameter outputs start at index 3
         self.assertEqual(
-            et.executorch_program.execution_plan[3]  # pyre-ignore
+            et.executorch_program.execution_plan[3]
             .values[0]
             .val.int_val,
             3,

exir/tests/test_remove_view_copy.py

@@ -197,23 +197,23 @@ def test_spec(self) -> None:
         self.assertEqual(len(instructions), 7)
 
         self.assertEqual(
-            instructions[0].instr_args.op_index, 0  # pyre-ignore
+            instructions[0].instr_args.op_index, 0
         )  # view @ idx2
         self.assertEqual(
-            instructions[1].instr_args.op_index, 0  # pyre-ignore
+            instructions[1].instr_args.op_index, 0
         )  # view @ idx3
         self.assertEqual(
-            instructions[2].instr_args.op_index, 1  # pyre-ignore
+            instructions[2].instr_args.op_index, 1
         )  # aten:mul @ idx6
         self.assertEqual(
-            instructions[3].instr_args.op_index, 0  # pyre-ignore
+            instructions[3].instr_args.op_index, 0
         )  # view @ idx7
         self.assertEqual(
-            instructions[4].instr_args.op_index, 1  # pyre-ignore
+            instructions[4].instr_args.op_index, 1
         )  # aten:mul @ idx9
         self.assertEqual(
-            instructions[5].instr_args.op_index, 2  # pyre-ignore
+            instructions[5].instr_args.op_index, 2
         )  # aten:view_copy @ idx11
         self.assertEqual(
-            instructions[6].instr_args.op_index, 2  # pyre-ignore
+            instructions[6].instr_args.op_index, 2
         )  # aten:view_copy @ idx11

extension/llm/export/quantizer_lib.py

@@ -184,14 +184,12 @@ def get_qnn_quantizer(
         )
         qnn_quantizer.set_per_channel_conv_quant(enable=False)
         qnn_quantizer.set_per_channel_linear_quant(enable=False)
-        # pyre-ignore: Undefined attribute [16]: Module `executorch.backends` has no attribute `qualcomm`.
         qnn_quantizer.set_quant_config(
             quant_dtype, is_qat=is_qat, act_observer=MinMaxObserver
         )
     elif quant_config == "16a4w":
         # pyre-ignore: Undefined attribute [16]: Module `executorch.backends` has no attribute `qualcomm`.
         quant_dtype = QuantDtype.use_16a4w
-        # pyre-ignore: Undefined attribute [16]: Module `executorch.backends` has no attribute `qualcomm`.
         qnn_quantizer.set_quant_config(
             quant_dtype, is_qat=is_qat, act_observer=MinMaxObserver
         )