typing for decorators - fx/_compatibility

backends/cadence/aot/compiler.py

@@ -60,13 +60,13 @@ def convert_pt2(
     # Export with dynamo
     model_gm = capture_pre_autograd_graph(model, inputs)
 
-    if model_gm_has_SDPA(model_gm):
+    if model_gm_has_SDPA(model_gm):  # pyre-fixme[6]
         # Decompose SDPA
-        DecomposeScaledDotProductAttention(False)(model_gm)
+        DecomposeScaledDotProductAttention(False)(model_gm)  # pyre-fixme[6]
 
         # Swap _safe_softmax with _softmax (see https://github.com/pytorch/pytorch/pull/133882
         # for details).
-        result = ReplaceSafeSoftmaxWithSoftmax()(model_gm)
+        result = ReplaceSafeSoftmaxWithSoftmax()(model_gm)  # pyre-fixme[6]
         assert result is not None
         model_gm = result.graph_module
 

backends/transforms/addmm_mm_to_linear.py

@@ -130,7 +130,7 @@ def replace_addmm_mm_with_linear(graph: torch.fx.Graph) -> torch.fx.Graph:
                         "call_function", ops.aten.linear.default, args
                     )
                     node.replace_all_uses_with(linear_node)
-                    output_val = linear_node.target(
+                    output_val = linear_node.target(  # pyre-fixme[29]
                         args[0].meta["val"], args[1].meta["val"], args[2].meta["val"]
                     )
                 else:
@@ -147,7 +147,7 @@ def replace_addmm_mm_with_linear(graph: torch.fx.Graph) -> torch.fx.Graph:
                         "call_function", ops.aten.linear.default, args
                     )
                     node.replace_all_uses_with(linear_node)
-                    output_val = linear_node.target(
+                    output_val = linear_node.target(  # pyre-fixme[29]
                         args[0].meta["val"], args[1].meta["val"]
                     )
                 linear_node.meta = node.meta

backends/transforms/decompose_sdpa.py

@@ -34,7 +34,7 @@ def call(
                 # refer to pytorch/test/test_decomp.py
                 decomposed_module = make_fx(
                     node.target,
-                    decomposition_table=get_decompositions(
+                    decomposition_table=get_decompositions(  # pyre-fixme[6]
                         [
                             torch.ops.aten._scaled_dot_product_flash_attention_for_cpu.default,
                         ]

backends/xnnpack/passes/channels_last_tagged_reshape_pass.py

@@ -124,7 +124,7 @@ def create_call_function_node(
             "call_function",
             target=target,
             args=args,
-            kwargs=(
+            kwargs=(  # pyre-fixme[6]
                 {"memory_format": memory_format} if memory_format is not None else {}
             ),
         )

backends/xnnpack/passes/convert_to_sdpa.py

@@ -83,7 +83,7 @@ def create_sdpa(
                 kwargs={"scale": scale},
             )
 
-        sdpa_node.meta["val"] = sdpa_node.target(
+        sdpa_node.meta["val"] = sdpa_node.target(  # pyre-fixme[29]
             *[n.meta["val"] for n in match.placeholder_nodes],
             scale=scale,
         )

examples/models/llama2/source_transformation/quantize.py

@@ -96,7 +96,7 @@ def quantize(
 
         try:
             # torchao 0.3+
-            from torchao._eval import InputRecorder
+            from torchao._eval import InputRecorder  # pyre-fixme[21]
         except ImportError:
             from torchao.quantization.GPTQ import InputRecorder  # pyre-ignore
 
@@ -110,7 +110,7 @@ def quantize(
         )
 
         inputs = (
-            InputRecorder(
+            InputRecorder(  # pyre-fixme[16]
                 tokenizer,
                 calibration_seq_length,
                 None,  # input_prep_func

examples/models/phi-3-mini/export_phi-3-mini.py

@@ -67,7 +67,7 @@ def export(args) -> None:
         model = capture_pre_autograd_graph(
             model, example_inputs, dynamic_shapes=dynamic_shapes
         )
-        model = prepare_pt2e(model, xnnpack_quantizer)
+        model = prepare_pt2e(model, xnnpack_quantizer)  # pyre-fixme[6]
         model(*example_inputs)
         model = convert_pt2e(model, fold_quantize=False)
         DuplicateDynamicQuantChainPass()(model)

exir/emit/_emitter.py

@@ -1270,7 +1270,7 @@ def _emit_prim_getters(self, prim_getters: Dict[str, Any]) -> List[ExecutionPlan
 
     def fetch_attr(self, target: _Target) -> _AbstractValue:
         """Fetch weights and other module parameters. If the attribute is a tensor, emit it."""
-        attr = super().fetch_attr(target)
+        attr = super().fetch_attr(target)  # pyre-fixme[6]
 
         if isinstance(attr, torch.Tensor):
             return self._emit_evalue(
@@ -1286,23 +1286,23 @@ def fetch_attr(self, target: _Target) -> _AbstractValue:
         else:
             return attr
 
-    def call_module(
+    def call_module(  # pyre-fixme[14]
         self, target: _Target, args: Tuple[_Argument, ...], kwargs: Dict[str, _Argument]
     ) -> None:
         """Unsupported in execution IR, so unhandled by the emitter."""
         raise InternalError(
             self._emit_node_specific_error(self.node, "call_module is not supported")
         )
 
-    def call_method(
+    def call_method(  # pyre-fixme[14]
         self, target: _Target, args: Tuple[_Argument, ...], kwargs: Dict[str, _Argument]
     ) -> _EmitterValue:
         """Unsupported in execution IR, so unhandled by the emitter."""
         raise InternalError(
             self._emit_node_specific_error(self.node, "call_method is not supported")
         )
 
-    def placeholder(
+    def placeholder(  # pyre-fixme[14]
         self, target: _Target, args: Tuple[_Argument, ...], kwargs: Dict[str, _Argument]
     ) -> _AbstractValue:
         """Performs actions for the placeholder node of a graph module.
@@ -1324,7 +1324,7 @@ def placeholder(
         self.placeholder_count += 1
         return value
 
-    def output(
+    def output(  # pyre-fixme[14]
         self, target: _Target, args: Tuple[_Argument, ...], kwargs: Dict[str, _Argument]
     ) -> None:
         """Performs actions for the output node of a graph module.
@@ -1354,7 +1354,7 @@ def output(
                     )
                     self.chain.instructions.append(instruction)
 
-    def call_function(
+    def call_function(  # pyre-fixme[14]
         self, target: _Target, args: Tuple[_Argument, ...], kwargs: Dict[str, _Argument]
     ) -> _EmitterValue:
         """Performs actions for the call_function node of a graph module.
@@ -1412,7 +1412,7 @@ def call_function(
                 )
             )
 
-    def run(
+    def run(  # pyre-fixme[14]
         self,
         *args: _Argument,
         initial_env: Optional[Dict[torch.fx.Node, _Argument]] = None,

exir/lowered_backend_module.py

@@ -139,7 +139,7 @@ def buffer(
         segment_alignment: int = 4096,
         constant_tensor_alignment: Optional[int] = None,
         delegate_alignment: Optional[int] = None,
-        memory_planning: MemoryPlanningPass = None,
+        memory_planning: MemoryPlanningPass = None,  # pyre-fixme[9]
     ) -> bytes:
         """
         Returns a buffer containing the serialized ExecuTorch binary.
@@ -161,7 +161,7 @@ def buffer(
     def program(
         self,
         emit_stacktrace: bool = False,
-        memory_planning: MemoryPlanningPass = None,
+        memory_planning: MemoryPlanningPass = None,  # pyre-fixme[9]
     ) -> Program:
         # Fix autodpes introuces cyclic dependencies:
         # program -> verifier -> lowered_backend_module -> program

exir/pass_base.py

@@ -177,7 +177,7 @@ def __init__(self, callback: "_ExportPassBase", codegen: CodeGen) -> None:
             self.fake_tensor_mode: Optional[FakeTensorMode] = None
             self.submodules: Dict[torch.nn.Module, str] = {}
 
-        def trace(self) -> None:
+        def trace(self) -> None:  # pyre-fixme[14,15]
             raise ExportPassBaseError("ExportTracer doesn't support trace().")
 
         def create_arg(self, a: Argument) -> torch.fx.Node:
@@ -290,7 +290,7 @@ def __init__(self, callback: "_ExportPassBase", gm: fx.GraphModule) -> None:
             self.callback = callback
             self.node: torch.fx.Node = next(iter(gm.graph.nodes))
 
-        def placeholder(
+        def placeholder(  # pyre-fixme[14]
             self,
             target: str,
             args: Tuple[Argument, ...],
@@ -351,7 +351,7 @@ def call_function(
             else:
                 raise ExportPassBaseError(f"Unsupported target type: {target}")
 
-        def get_attr(
+        def get_attr(  # pyre-fixme[14]
             self, target: str, args: Tuple[Argument, ...], kwargs: Dict[str, Argument]
         ) -> Argument:
             return super().get_attr(target, args, kwargs)
@@ -364,7 +364,7 @@ def call_module(
         ) -> None:
             raise ExportPassBaseError("call_module is not supported.")
 
-        def call_method(
+        def call_method(  # pyre-fixme[14]
             self, target: str, args: Tuple[Argument, ...], kwargs: Dict[str, Argument]
         ) -> None:
             raise ExportPassBaseError("call_method is not supported.")

exir/passes/__init__.py

@@ -302,6 +302,7 @@ def make_alloc_node(
                     "Memory allocator node needs FakeTensor val or TensorMetadata to proceed"
                 )
 
+    # pyre-fixme[6]
     alloc = graph_module.graph.call_function(memory.alloc, (alloc_spec,))
     alloc.meta["val"] = val
     alloc.meta["tensor_meta"] = tensor_meta

exir/passes/remove_noop_pass.py

@@ -40,7 +40,7 @@ def eliminate_dq_q(
                 qparams_q = list(user.args)[1:]
                 if qparams_dq != qparams_q:
                     continue
-                user.replace_all_uses_with(node.args[0])
+                user.replace_all_uses_with(node.args[0])  # pyre-fixme[6]
 
 
 class RemoveNoopPass(ExportPass):

exir/tests/test_passes.py

@@ -1421,7 +1421,7 @@ def quantize_model(
             quantizer = XNNPACKQuantizer()
             quantization_config = get_symmetric_quantization_config()
             quantizer.set_global(quantization_config)
-            m = prepare_pt2e(m, quantizer)
+            m = prepare_pt2e(m, quantizer)  # pyre-fixme[6]
             m = convert_pt2e(m, fold_quantize=True)
             ep = torch.export.export(m, example_inputs)
             dq_nodes_pre = count_dq_nodes(ep.graph_module)

exir/tests/test_quantization.py

@@ -58,7 +58,7 @@ def test_resnet(self) -> None:
             quantizer = XNNPACKQuantizer()
             operator_config = get_symmetric_quantization_config(is_per_channel=True)
             quantizer.set_global(operator_config)
-            m = prepare_pt2e(m, quantizer)
+            m = prepare_pt2e(m, quantizer)  # pyre-fixme[6]
             self.assertEqual(
                 id(m.activation_post_process_3), id(m.activation_post_process_2)
             )

exir/tracer.py

@@ -272,7 +272,7 @@ def __torch_function__(
             kwargs = {}
         if torch.is_inference_mode_enabled():
             if func is torch.nn.functional.layer_norm:
-                args, kwargs = normalize_function(func, args, kwargs)
+                args, kwargs = normalize_function(func, args, kwargs)  # pyre-fixme[23]
                 input, normalized_shape = args
                 normalized_shape = list(normalized_shape)
                 return cls.__torch_dispatch__(
@@ -470,13 +470,13 @@ def create_arg(self, a: Value) -> torch.fx.Node:  # noqa: C901
                 self.submodules[a] = name_submodule
             return self.create_node("get_attr", self.submodules[a], (), {})
 
-        return super().create_arg(a)
+        return super().create_arg(a)  # pyre-fixme[7]
 
     @staticmethod
     def get() -> "DispatchTracer":
         return TRACER
 
-    def trace(
+    def trace(  # pyre-fixme[14,15]
         self,
         root: Callable[..., Value],
         concrete_args: Tuple[Value, ...] = (),

exir/verification/arg_validator.py

@@ -62,7 +62,7 @@ def _get_kernel_arg(self, schema_arg, schema_arg_idx, args, kwargs):
 
         return kernel_arg
 
-    def call_function(  # noqa: C901
+    def call_function(  # noqa: C901  # pyre-fixme[14]
         self, target: _Target, args: Tuple[_Argument, ...], kwargs: Dict[str, _Argument]
     ) -> Any:
         """
@@ -73,7 +73,7 @@ def call_function(  # noqa: C901
         ):
             if isinstance(target, HigherOrderOperator):
                 raise RunHigherOrderOperatorError("Can't run delegate")
-            return super().call_function(target, args, kwargs)
+            return super().call_function(target, args, kwargs)  # pyre-fixme[6]
 
         # TODO(gasoonjia): Update Optional[torch.dtype] to a concrete class to support mixed dtypes in tensorlist.
         tensor_arg_types: Dict[str, Optional[torch.dtype]] = {}
@@ -126,4 +126,4 @@ def call_function(  # noqa: C901
         valid = target._schema.dtype_constraint.validate(tensor_arg_types)
         if not valid:
             self.violating_ops[target] = tensor_arg_types
-        return super().call_function(target, args, kwargs)
+        return super().call_function(target, args, kwargs)  # pyre-fixme[6]

extension/llm/export/builder.py

@@ -161,6 +161,7 @@ def capture_pre_autograd_graph(self) -> "LLMEdgeManager":
         # 1. torch.nn.attention.sdpa_kernel([SDPBackend.MATH]) is for bypassing the dynamo error when tracing
         # 2. torch.no_grad() is for getting rid of the dropout (not sure why training ops will show up)
         with torch.nn.attention.sdpa_kernel([SDPBackend.MATH]), torch.no_grad():
+            # pyre-fixme[8]
             self.pre_autograd_graph_module = capture_pre_autograd_graph(
                 self.model, self.example_inputs, dynamic_shapes=dynamic_shape
             )
@@ -209,11 +210,12 @@ def export_to_edge(self) -> "LLMEdgeManager":
         # 2. torch.no_grad() is for getting rid of the dropout (not sure why training ops will show up)
         with torch.nn.attention.sdpa_kernel([SDPBackend.MATH]), torch.no_grad():
             if self.pre_autograd_graph_module is None:
+                # pyre-fixme[8]
                 self.pre_autograd_graph_module = capture_pre_autograd_graph(
                     self.model, self.example_inputs, dynamic_shapes=dynamic_shape
                 )
             self.edge_manager = export_to_edge(
-                self.pre_autograd_graph_module,
+                self.pre_autograd_graph_module,  # pyre-fixme[6]
                 self.example_inputs,
                 dynamic_shapes=dynamic_shape,
                 edge_constant_methods=self.metadata,

extension/llm/export/partitioner_lib.py

@@ -52,7 +52,7 @@ def get_mps_partitioner(use_kv_cache: bool = False):
         )
 
     compile_specs = [CompileSpec("use_fp16", bytes([True]))]
-    return MPSPartitioner(compile_specs)
+    return MPSPartitioner(compile_specs)  # pyre-fixme[16]
 
 
 def get_coreml_partitioner(
@@ -92,14 +92,14 @@ def get_coreml_partitioner(
     # if use_kv_cache:
     #     minimum_deployment_target = max(minimum_deployment_target, ct.target.iOS18)
 
-    compile_specs = CoreMLBackend.generate_compile_specs(
+    compile_specs = CoreMLBackend.generate_compile_specs(  # pyre-fixme[16]
         minimum_deployment_target=minimum_deployment_target,
         compute_precision=ct.precision(ct.precision.FLOAT16.value),
         # using `ComputeUnit.ALL` can increase the model load time, default to `ComputeUnit.CPU_AND_GPU`
         compute_unit=ct.ComputeUnit[ct.ComputeUnit.CPU_AND_GPU.name.upper()],
-        model_type=CoreMLBackend.MODEL_TYPE.MODEL,
+        model_type=CoreMLBackend.MODEL_TYPE.MODEL,  # pyre-fixme[16]
     )
-    return CoreMLPartitioner(
+    return CoreMLPartitioner(  # pyre-fixme[16]
         compile_specs=compile_specs,
     )
 
@@ -136,9 +136,10 @@ def get_qnn_partitioner(
     if pt2e_quantize is not None:
         use_fp16 = False
 
-    return QnnPartitioner(
-        generate_qnn_executorch_compiler_spec(
-            soc_model=QcomChipset.SM8650,  # default to SM8650
+    return QnnPartitioner(  # pyre-fixme[16]
+        generate_qnn_executorch_compiler_spec(  # pyre-fixme[16]
+            soc_model=QcomChipset.SM8650,  # default to SM8650  # pyre-fixme[16]
+            # pyre-fixme[16]
             backend_options=generate_htp_compiler_spec(use_fp16=use_fp16),
             debug=False,
             saver=False,

extension/llm/export/quantizer_lib.py

@@ -146,7 +146,7 @@ def get_qnn_quantizer(
     quantization_mode: Optional[str] = None,
 ):
     try:
-        from executorch.backends.qualcomm.quantizer.custom_annotation import (
+        from executorch.backends.qualcomm.quantizer.custom_annotation import (  # pyre-fixme[21]
             custom_annotate_llama_matmul_16a8w,
         )
 
@@ -168,15 +168,15 @@ def get_qnn_quantizer(
     assert (
         backend == "qnn"
     ), f"The quantization config is for backend {backend} instead of qnn."
-    qnn_quantizer = QnnQuantizer()
+    qnn_quantizer = QnnQuantizer()  # pyre-fixme[16]
     qnn_quantizer.set_per_channel_conv_quant(enable=True)
     qnn_quantizer.set_per_channel_linear_quant(enable=True)
     # more custom quantization are supported including 16a4w etc. default to 8bit quantized
     custom_annotations = ()
     if quant_config == "8a8w":
-        quant_dtype = QuantDtype.use_8a8w
+        quant_dtype = QuantDtype.use_8a8w  # pyre-fixme[16]
     elif quant_config == "16a16w":
-        quant_dtype = QuantDtype.use_16a16w
+        quant_dtype = QuantDtype.use_16a16w  # pyre-fixme[16]
         qnn_quantizer.add_16bit_quant_ops(qnn_quantizer.SUPPORTED_OPS)
         qnn_quantizer.set_bit16_op_quant_config(
             # pyre-ignore: Undefined attribute [16]: Module `executorch.backends` has no attribute `qualcomm`.