Arm backend: Move q/dq ops constants to backends/arm/constants.py

backends/arm/_passes/annotate_decomposed_matmul.py

@@ -12,7 +12,7 @@
 import torch
 from executorch.backends.arm._passes.arm_pass_utils import create_node
 
-from executorch.backends.arm.tosa_quant_utils import dq_ops, q_ops
+from executorch.backends.arm.constants import DQ_OPS, Q_OPS
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.dialects.edge._ops import EdgeOpOverload
 from executorch.exir.pass_base import ExportPass, PassResult
@@ -62,7 +62,7 @@ def call(self, graph_module: GraphModule) -> PassResult:
         }
         for partition in matmul_partitions:
             quantized_input = all(
-                input_node.target in dq_ops for input_node in partition.input_nodes
+                input_node.target in DQ_OPS for input_node in partition.input_nodes
             )
             matmul_node = [
                 node for node in partition.nodes if node.target in matmul_targets
@@ -93,7 +93,7 @@ def call(self, graph_module: GraphModule) -> PassResult:
                     graph_module.graph.erase_node(partition_input)
 
             partition_output = list(partition.output_nodes[0].users)[0]
-            quantized_output = partition_output.target in q_ops
+            quantized_output = partition_output.target in Q_OPS
             if quantized_output:
                 with graph_module.graph.inserting_after(matmul_node):
                     # Create q-node after matmul

backends/arm/_passes/fold_qdq_with_annotated_qparams_pass.py

@@ -15,8 +15,9 @@
     get_param_tensor,
     is_param_node,
 )
+from executorch.backends.arm.constants import DQ_OPS, Q_OPS
 
-from executorch.backends.arm.tosa_quant_utils import dq_ops, q_ops, QuantArgs
+from executorch.backends.arm.tosa_quant_utils import QuantArgs
 
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.dialects.edge._ops import EdgeOpOverload
@@ -109,7 +110,7 @@ def fold_and_annotate_arg(
                 return
 
             arg_quant_params = None
-            if arg.target in dq_ops:
+            if arg.target in DQ_OPS:
                 args = arg.args
                 scales = args[1]
                 if (
@@ -137,9 +138,9 @@ def fold_and_annotate_arg(
         if input_qparams is not None:
             node.meta["input_qparams"][i] = input_qparams
             for n in nodes_to_remove:
-                if n.target not in dq_ops:
+                if n.target not in DQ_OPS:
                     raise RuntimeError(
-                        f"Expected one of {dq_ops} dq_op, got {n.target}"
+                        f"Expected one of {DQ_OPS} dq_op, got {n.target}"
                     )
 
                 node.replace_input_with(n, cast(Node, n.args[0]))
@@ -154,7 +155,7 @@ def call(self, graph_module: GraphModule) -> PassResult:
             if n.op != "call_function":
                 continue
             # Don't fold chains of quant-ops into each other.
-            if n.target in (*q_ops, *dq_ops):
+            if n.target in (*Q_OPS, *DQ_OPS):
                 continue
 
             # Make sure we haven't already set qparams meta information on the node
@@ -184,7 +185,7 @@ def call(self, graph_module: GraphModule) -> PassResult:
             # Copy the users, since we are modifying it.
             users_copy = copy.copy(n.users)
             for i, user in enumerate(users_copy):
-                if user.target not in q_ops:
+                if user.target not in Q_OPS:
                     continue
 
                 # quantization node found here, store the quantization parameters in meta value
@@ -221,7 +222,7 @@ def call(self, graph_module: GraphModule) -> PassResult:
 
             # Make sure we have a quantized operator
             user = list(n.users)[0]
-            if user.target not in q_ops:
+            if user.target not in Q_OPS:
                 continue
 
             qargs = QuantArgs.from_operator(user.target, user.args)

backends/arm/_passes/fuse_quantized_activation_pass.py

@@ -6,7 +6,8 @@
 # pyre-unsafe
 
 import torch
-from executorch.backends.arm.tosa_quant_utils import q_ops, QuantArgs
+from executorch.backends.arm.constants import Q_OPS
+from executorch.backends.arm.tosa_quant_utils import QuantArgs
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_base import ExportPass, PassResult
 from torch.fx import Node
@@ -21,7 +22,7 @@ def _is_fuseable_quantized_activation(node: Node):
             min_val = node.args[1]
             is_fuseable = min_val == 0
 
-        is_quantized = len(node.users) == 1 and next(iter(node.users)).target in q_ops
+        is_quantized = len(node.users) == 1 and next(iter(node.users)).target in Q_OPS
         if is_fuseable and is_quantized:
             quant_node = next(iter(node.users))
             quant_args = QuantArgs.from_operator(quant_node.target, quant_node.args)

backends/arm/_passes/insert_rescales_pass.py

@@ -9,7 +9,8 @@
 
 import torch
 from executorch.backends.arm._passes.arm_pass_utils import create_node
-from executorch.backends.arm.tosa_quant_utils import dq_ops, q_ops, QuantArgs
+from executorch.backends.arm.constants import DQ_OPS, Q_OPS
+from executorch.backends.arm.tosa_quant_utils import QuantArgs
 from executorch.exir.pass_base import ExportPass, PassResult
 from torch import Tensor
 from torch.fx import GraphModule, Node
@@ -94,11 +95,11 @@ def call(self, graph_module: GraphModule) -> PassResult:
         for node in graph_module.graph.nodes:
             node = cast(Node, node)
 
-            if node.target not in dq_ops:
+            if node.target not in DQ_OPS:
                 continue
             # Copy users since we remove them while iterating, modyfing the node.users list.
             for user in copy(node.users):
-                if user.target in q_ops:
+                if user.target in Q_OPS:
                     self.fold_dq_q_to_rescale(node, user, graph_module)
                     modified = True
             if len(node.users) == 0:

backends/arm/_passes/mm_to_bmm_pass.py

@@ -12,7 +12,7 @@
     get_first_fake_tensor,
     insert_q_dq_pair,
 )
-from executorch.backends.arm.tosa_quant_utils import dq_ops, q_ops
+from executorch.backends.arm.constants import DQ_OPS, Q_OPS
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_base import ExportPass, PassResult
 from torch.fx import Node
@@ -56,7 +56,7 @@ def call(self, graph_module: torch.fx.GraphModule):
                     node.replace_input_with(input_node, unsqueeze_before)
 
                 # If Quantized we must insert unsqueeze --> q --> dq --> node
-                if input_node.target in dq_ops:
+                if input_node.target in DQ_OPS:
                     q_params = input_node.args[1:]
                     insert_q_dq_pair(graph, unsqueeze_before, q_params, from_node=node)
 
@@ -89,7 +89,7 @@ def call(self, graph_module: torch.fx.GraphModule):
                     user.replace_input_with(bmm_node, squeeze_after)
 
             # If quantized, insert mm --> q --> dq --> squeeze
-            if all(original_user.target in q_ops for original_user in original_users):
+            if all(original_user.target in Q_OPS for original_user in original_users):
                 q_params = original_users[0].args[1:]
                 insert_q_dq_pair(graph, bmm_node, q_params, from_node=node)
 

backends/arm/constants.py

@@ -0,0 +1,31 @@
+# Copyright 2025 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.
+
+from typing import Any, cast, Final
+
+from executorch.exir.dialects._ops import ops as exir_ops
+
+exir_ops = cast(Any, exir_ops)
+
+qd = exir_ops.edge.quantized_decomposed
+
+QUANT_PER_TENSOR_OP: Final = qd.quantize_per_tensor.default
+QUANT_PER_TENSOR_OP_T: Final = qd.quantize_per_tensor.tensor
+QUANT_PER_CHANNEL_OP: Final = qd.quantize_per_channel.default
+
+DEQUANT_PER_TENSOR_OP: Final = qd.dequantize_per_tensor.default
+DEQUANT_PER_TENSOR_OP_T: Final = qd.dequantize_per_tensor.tensor
+DEQUANT_PER_CHANNEL_OP: Final = qd.dequantize_per_channel.default
+
+Q_OPS: Final = (QUANT_PER_TENSOR_OP, QUANT_PER_TENSOR_OP_T, QUANT_PER_CHANNEL_OP)
+DQ_OPS: Final = (DEQUANT_PER_TENSOR_OP, DEQUANT_PER_TENSOR_OP_T, DEQUANT_PER_CHANNEL_OP)
+
+PER_TENSOR_QDQ_OPS: Final = (
+    QUANT_PER_TENSOR_OP,
+    QUANT_PER_TENSOR_OP_T,
+    DEQUANT_PER_TENSOR_OP,
+    DEQUANT_PER_TENSOR_OP_T,
+)
+PER_CHANNEL_QDQ_OPS: Final = (QUANT_PER_CHANNEL_OP, DEQUANT_PER_CHANNEL_OP)

backends/arm/operator_support/tosa_supported_operators.py

@@ -19,13 +19,13 @@
     FuseQuantizedActivationPass,
 )
 from executorch.backends.arm._passes.insert_table_ops import TableOps
+from executorch.backends.arm.constants import DQ_OPS, Q_OPS
 from executorch.backends.arm.operator_support.ethos_u55_support import (
     EthosU55DtypeSupport,
     EthosU55NotSupported,
     EthosU55TransposeCheck,
     EthosU55ViewCheck,
 )
-from executorch.backends.arm.tosa_quant_utils import dq_ops, q_ops
 from executorch.backends.arm.tosa_specification import TosaSpecification
 from executorch.exir import ExportedProgram
 from executorch.exir.backend.utils import WhyNoPartitionReporter
@@ -368,7 +368,7 @@ def _is_matmul_node_supported(
                     matched_partition = partition
             if matched_partition is not None:
                 input_quantized = all(
-                    input_node.target in dq_ops
+                    input_node.target in DQ_OPS
                     for input_node in matched_partition.input_nodes
                 )
                 if not input_quantized:
@@ -377,7 +377,7 @@ def _is_matmul_node_supported(
                     )
                     return False
                 output_quantized = all(
-                    output_node_user.target in q_ops
+                    output_node_user.target in Q_OPS
                     for output_node_user in matched_partition.output_nodes[0].users
                 )
                 if not output_quantized:
@@ -413,7 +413,7 @@ def is_node_supported(
             users = node.users
             output_quantized = all(
                 user.target == operator.getitem
-                and all(user_user.target in q_ops for user_user in user.users)
+                and all(user_user.target in Q_OPS for user_user in user.users)
                 for user in users
             )
         elif FuseQuantizedActivationPass._is_fuseable_input(node):
@@ -427,7 +427,7 @@ def is_node_supported(
             input_quantized = FuseQuantizedActivationPass._is_fuseable_input(input_node)
 
         input_quantized = input_quantized or all(
-            (input_node.target in dq_ops)
+            (input_node.target in DQ_OPS)
             or (not get_first_fake_tensor(input_node).dtype.is_floating_point)
             for input_node in node.all_input_nodes
         )
@@ -436,7 +436,7 @@ def is_node_supported(
             self.reporter.report_reject(node, "One or more inputs were not quantized.")
             return False
 
-        all_q_users = all((output_node.target in q_ops) for output_node in node.users)
+        all_q_users = all((output_node.target in Q_OPS) for output_node in node.users)
         is_floating_point = get_first_fake_tensor(node).dtype.is_floating_point
         output_quantized = output_quantized or all_q_users or not is_floating_point
 

backends/arm/tosa_partitioner.py

@@ -9,6 +9,7 @@
 from typing import Callable, List, Optional, Sequence, Tuple
 
 import torch
+from executorch.backends.arm.constants import DQ_OPS, Q_OPS
 from executorch.backends.arm.arm_backend import (
     get_tosa_spec,
     is_tosa,
@@ -25,7 +26,6 @@
     PartitionResult,
 )
 from executorch.exir.backend.utils import tag_constant_data, WhyNoPartitionReporter
-from executorch.exir.dialects._ops import ops as exir_ops
 from torch.export.exported_program import ExportedProgram
 from torch.fx.passes.infra.partitioner import CapabilityBasedPartitioner
 from torch.fx.passes.operator_support import OperatorSupportBase
@@ -34,22 +34,6 @@
 logger = logging.getLogger(__name__)
 
 
-def is_quant_node(node: torch.fx.node.Node) -> bool:
-    return node.target in {
-        exir_ops.edge.quantized_decomposed.quantize_per_channel.default,
-        exir_ops.edge.quantized_decomposed.quantize_per_tensor.default,
-        exir_ops.edge.quantized_decomposed.quantize_per_tensor.tensor,
-    }
-
-
-def is_dequant_node(node: torch.fx.node.Node) -> bool:
-    return node.target in {
-        exir_ops.edge.quantized_decomposed.dequantize_per_channel.default,
-        exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default,
-        exir_ops.edge.quantized_decomposed.dequantize_per_tensor.tensor,
-    }
-
-
 class TOSAPartitioner(Partitioner):
     def __init__(
         self,
@@ -99,14 +83,14 @@ def is_partitioned(node: torch.fx.Node, tag=tag) -> bool:
             for node in exported_program.graph_module.graph.nodes:
                 if not is_partitioned(node):
                     continue
-                if is_quant_node(node):
+                if node.target in Q_OPS:
                     for input in node.all_input_nodes:
                         if not is_partitioned(input):
                             del node.meta["delegation_tag"]
                             break
                     continue
 
-                if is_dequant_node(node):
+                if node.target in DQ_OPS:
                     for user in node.users:
                         if not is_partitioned(user):
                             del node.meta["delegation_tag"]

backends/arm/tosa_quant_utils.py

@@ -15,6 +15,7 @@
 
 import torch.fx
 import torch.fx.node
+from executorch.backends.arm.constants import PER_CHANNEL_QDQ_OPS, PER_TENSOR_QDQ_OPS
 
 from executorch.backends.arm.tosa_mapping import TosaArg
 from executorch.exir.dialects._ops import ops as exir_ops
@@ -23,25 +24,6 @@
 from tosa.RoundingMode import RoundingMode  # type: ignore
 
 
-q_ops = (
-    exir_ops.edge.quantized_decomposed.quantize_per_tensor.default,
-    exir_ops.edge.quantized_decomposed.quantize_per_channel.default,
-)
-dq_ops = (
-    exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default,
-    exir_ops.edge.quantized_decomposed.dequantize_per_channel.default,
-)
-per_tensor_q_dq_ops = (
-    exir_ops.edge.quantized_decomposed.quantize_per_tensor.default,
-    exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default,
-)
-per_channel_q_dq_ops = (
-    exir_ops.edge.quantized_decomposed.quantize_per_channel.default,
-    exir_ops.edge.quantized_decomposed.dequantize_per_channel.default,
-)
-dq_q_ops = (*q_ops, *dq_ops)
-
-
 def insert_rescale_ops_to_int32(
     tosa_graph: Any,
     inputs: list[TosaArg],
@@ -185,7 +167,7 @@ def dequantize_value(self, qx: torch.Tensor) -> torch.Tensor:
 
     @classmethod
     def from_operator(cls, op, args):
-        if op in per_tensor_q_dq_ops:
+        if op in PER_TENSOR_QDQ_OPS:
             return cls(
                 scale=cast(float, args[1]),
                 zp=cast(int, args[2]),
@@ -195,7 +177,7 @@ def from_operator(cls, op, args):
                 axis=0,
                 per_channel=False,
             )
-        elif op in per_channel_q_dq_ops:
+        elif op in PER_CHANNEL_QDQ_OPS:
             return cls(
                 scale=cast(list[float], args[1].tolist()),
                 zp=cast(list[int], args[2].tolist()),