Solve circular import error

Move process_output, process_inputs and process_call_function to a
separate file named process_node. This removes the circular dependency
between tosa_utils.py and node_visitor.py.

Signed-off-by: Oscar Andersson <[email protected]>
Change-Id: I5bfe42ad6cd36c579390af6d7fa14c8fb7341842

Author: oscarandersson8218

backends/arm/arm_backend.py

@@ -18,18 +18,17 @@
 import serializer.tosa_serializer as ts
 from executorch.backends.arm.arm_vela import vela_compile
 from executorch.backends.arm.operators.node_visitor import get_node_visitors
-from executorch.backends.arm.operators.op_output import process_output
-from executorch.backends.arm.operators.op_placeholder import process_placeholder
 
 from executorch.backends.arm.tosa_specification import TosaSpecification
 from executorch.backends.arm._passes.arm_pass_manager import (
     ArmPassManager,
 )  # usort: skip
-from executorch.backends.arm.tosa_utils import (
-    dbg_fail,
-    dbg_tosa_dump,
+from executorch.backends.arm.process_node import (
     process_call_function,
+    process_output,
+    process_placeholder,
 )
+from executorch.backends.arm.tosa_utils import dbg_fail, dbg_tosa_dump
 from executorch.exir.backend.backend_details import BackendDetails, PreprocessResult
 from executorch.exir.backend.compile_spec_schema import CompileSpec
 from torch.export.exported_program import ExportedProgram

backends/arm/operators/op_output.py

@@ -1,28 +1,68 @@
-# Copyright 2023-2024 Arm Limited and/or its affiliates.
+# Copyright 2024 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
-
-# pyre-unsafe
+#
+from typing import cast, Dict
 
 import numpy as np
 import serializer.tosa_serializer as ts
+import torch
 import torch.fx
-from executorch.backends.arm.tosa_mapping import TosaArg
+from executorch.backends.arm.operators.node_visitor import NodeVisitor
+from executorch.backends.arm.tosa_mapping import map_dtype, TosaArg
 from executorch.backends.arm.tosa_quant_utils import (
     get_quant_arg_upstream,
     get_quantized_node_output_dtype,
     is_node_quantized,
 )
 from executorch.backends.arm.tosa_specification import TosaSpecification
 from executorch.backends.arm.tosa_utils import (
+    getNodeArgs,
     is_bias_node_for_quantized_conv,
-    map_dtype,
     tosa_shape,
 )
 from torch.export.exported_program import ExportedProgram
 
 
+def process_call_function(
+    node: torch.fx.Node,
+    tosa_graph: ts.TosaSerializer,
+    node_visitors: Dict[str, NodeVisitor],
+    tosa_spec: TosaSpecification,
+):
+    # Unpack arguments and convert
+    inputs = getNodeArgs(node)
+
+    # Convert output (this node itself)
+    output = TosaArg(node)
+
+    is_quant_node = is_node_quantized(node)
+    if is_quant_node:
+        output_dtype = map_dtype(get_quantized_node_output_dtype(node))
+    else:
+        output_dtype = output.dtype
+    tosa_graph.currRegion.currBasicBlock.addTensor(
+        output.name,
+        tosa_shape(output.shape, output.dim_order),
+        output_dtype,
+    )
+
+    # Visiting each Node
+    # pyre-ignore[16]: Undefined attribute.
+    if node.target.__name__ in node_visitors:
+        # pyre-ignore[16]: Undefined attribute.
+        node_visitors[node.target.__name__].define_node(
+            node,
+            tosa_graph,
+            inputs,
+            output,
+            is_quant_node,
+        )
+    else:
+        raise RuntimeError(f"Unknown operator {node.target} for TOSA : {tosa_spec}")
+
+
 def process_inputs(
     node: torch.fx.Node,
     tosa_graph: ts.TosaSerializer,
@@ -176,3 +216,13 @@ def process_placeholder(
         )
     else:
         raise RuntimeError(f"Placeholder '{node.name}' is of unknown type.")
+
+
+def process_output(
+    node: torch.fx.Node,
+    tosa_graph: ts.TosaSerializer,
+):
+    for output in cast(tuple[torch.fx.Node, ...], node.args[0]):
+        tosa_graph.addOutputTensor(
+            tosa_graph.currRegion.currBasicBlock.tensors[output.name]
+        )

backends/arm/operators/op_placeholder.py renamed to backends/arm/process_node.py

@@ -7,22 +7,18 @@
 
 import logging
 import os
-from typing import Any, cast, Dict
+from typing import Any, cast
 
 import numpy as np
 import serializer.tosa_serializer as ts
 import torch
-from executorch.backends.arm.operators.node_visitor import NodeVisitor
-from executorch.backends.arm.tosa_mapping import map_dtype, TosaArg
+from executorch.backends.arm.tosa_mapping import TosaArg
 
 from executorch.backends.arm.tosa_quant_utils import (
     get_quant_arg_downstream,
     get_quant_arg_upstream,
-    get_quantized_node_output_dtype,
-    is_node_quantized,
     q_op,
 )
-from executorch.backends.arm.tosa_specification import TosaSpecification
 from executorch.exir.dialects._ops import ops as exir_ops
 from serializer.tosa_serializer import TosaOp
 from torch.fx import Node
@@ -233,44 +229,6 @@ def tosa_shape(shape, dim_order):
     return tuple([shape[dim] for dim in dim_order])
 
 
-def process_call_function(
-    node: torch.fx.Node,
-    tosa_graph: ts.TosaSerializer,
-    node_visitors: Dict[str, NodeVisitor],
-    tosa_spec: TosaSpecification,
-):
-    # Unpack arguments and convert
-    inputs = getNodeArgs(node)
-
-    # Convert output (this node itself)
-    output = TosaArg(node)
-
-    is_quant_node = is_node_quantized(node)
-    if is_quant_node:
-        output_dtype = map_dtype(get_quantized_node_output_dtype(node))
-    else:
-        output_dtype = output.dtype
-    tosa_graph.currRegion.currBasicBlock.addTensor(
-        output.name,
-        (tosa_shape(output.shape, output.dim_order)),
-        output_dtype,
-    )
-
-    # Visiting each Node
-    # pyre-ignore[16]: Undefined attribute.
-    if node.target.__name__ in node_visitors:
-        # pyre-ignore[16]: Undefined attribute.
-        node_visitors[node.target.__name__].define_node(
-            node,
-            tosa_graph,
-            inputs,
-            output,
-            is_quant_node,
-        )
-    else:
-        raise RuntimeError(f"Unknown operator {node.target} for TOSA : {tosa_spec}")
-
-
 def expand_dims(
     tosa_graph: ts.TosaSerializer,
     input_node: TosaArg,