Revert "Arm backend: Propagate node info from quantizer to backend"

backends/arm/operator_support/tosa_supported_operators.py

@@ -21,7 +21,6 @@
     FuseQuantizedActivationPass,
 )
 from executorch.backends.arm._passes.insert_table_ops import TableOps
-from executorch.backends.arm.common.annotation_meta import ArmAnnotationInfo
 from executorch.backends.arm.constants import DQ_OPS, MAX_RANK, Q_OPS
 from executorch.backends.arm.operator_support.ethos_u55_support import (
     EthosU55CastCheck,
@@ -141,7 +140,6 @@ def tosa_support_factory(
     ]
 
     if not tosa_spec.support_float():
-        negative_checks.append(CheckArmQuantized(reporter))
         negative_checks.append(CheckProperQuantization(reporter))
     if tosa_spec.is_U55_subset:
         negative_checks.append(EthosU55NotSupported(reporter))
@@ -169,6 +167,7 @@ class TOSAProINTSupportList(OperatorSupportBase):
     def is_node_supported(
         self, submodules: typing.Mapping[str, torch.nn.Module], node: fx.Node
     ) -> bool:
+
         return node.op == "call_function" and node.target in TOSA_PRO_INT_SupportList
 
 
@@ -181,78 +180,8 @@ class TOSAProFPSupportList(OperatorSupportBase):
     def is_node_supported(
         self, submodules: typing.Mapping[str, torch.nn.Module], node: fx.Node
     ) -> bool:
-        return node.op == "call_function" and node.target in TOSA_PRO_FP_SupportList
-
-
-class CheckArmQuantized(OperatorSupportBase):
-    """
-    Check if the node was marked as quantized in the Arm backend.
-    This is used to ensure that nodes that were quantized in the Arm backend
-    are only partitioned if they are supported by the TOSA backend.
-    """
-
-    def __init__(self, reporter: WhyNoPartitionReporter):
-        self.reporter = reporter
-
-    def _is_quantized(self, node: torch.fx.Node) -> bool:
-        """Checks if the node is quantized.
 
-        A node is considered quantized if at least one criteria is met:
-        - Its dtype is not floating point or complex => integer
-        - It is one of the special cases where the node has been created in to_edge, e.g.
-          .Scalar operations that have been promoted .Tensor operations
-          where the scalar is replaced by a full op.
-        - It has been marked as quantized in the ArmAnnotationInfo custom meta.
-
-        Args:
-            node (torch.fx.Node): The FX node to check.
-
-        Returns:
-            bool: True if the node is quantized, False otherwise.
-        """
-        node_dtype = get_first_fake_tensor(node).dtype
-        if not node_dtype.is_complex and not node_dtype.is_floating_point:
-            return True
-        if node.target in (
-            exir_ops.edge.aten.full_like.default,
-            *ComputeConstantOpsAOT.targeted_ops,
-        ):
-            # Special cases where nodes have been created in to_edge, e.g.
-            # .Scalar operations that have been promoted .Tensor operations
-            # where the scalar is replaced by a full op.
-            if all(user.target in Q_OPS for user in node.users):
-                return True
-            for user in node.users:
-                if (
-                    user.target
-                    == exir_ops.edge.dim_order_ops._to_dim_order_copy.default
-                ):
-                    dim_order_dtype = get_first_fake_tensor(user).dtype
-                    if dim_order_dtype.is_complex or dim_order_dtype.is_floating_point:
-                        return False
-                else:
-                    return False
-            return True
-        return (
-            ArmAnnotationInfo.CUSTOM_META_KEY in node.meta.get("custom", {})
-            and node.meta["custom"][ArmAnnotationInfo.CUSTOM_META_KEY].quantized
-        )
-
-    def is_node_supported(
-        self, submodules: typing.Mapping[str, torch.nn.Module], node: fx.Node
-    ) -> bool:
-        if node.op != "call_function":
-            return False
-
-        if node.target in (*DQ_OPS, *Q_OPS):
-            return True
-
-        if not self._is_quantized(node):
-            self.reporter.report_reject(
-                node, "Node was not marked as quantized in the Arm backend."
-            )
-            return False
-        return True
+        return node.op == "call_function" and node.target in TOSA_PRO_FP_SupportList
 
 
 class CheckProperQuantization(OperatorSupportBase):
@@ -498,6 +427,7 @@ def is_node_supported(
 
 
 class CheckFloat64Inputs(OperatorSupportBase):
+
     def __init__(
         self, exported_program: ExportedProgram, reporter: WhyNoPartitionReporter
     ):

backends/arm/quantizer/arm_quantizer_utils.py

@@ -1,6 +1,6 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
 # Copyright 2024-2025 Arm Limited and/or its affiliates.
+# All rights reserved.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -14,8 +14,6 @@
 
 from typing import cast
 
-from executorch.backends.arm.common.annotation_meta import ArmAnnotationInfo
-
 from torch.fx import Node
 
 from torchao.quantization.pt2e.quantizer import QuantizationAnnotation
@@ -67,10 +65,4 @@ def mark_node_as_annotated(node: Node) -> None:
     """
     if Q_ANNOTATION_KEY not in node.meta:
         node.meta[Q_ANNOTATION_KEY] = QuantizationAnnotation()
-    annotation_info = ArmAnnotationInfo(
-        quantized=True,
-    )
     node.meta[Q_ANNOTATION_KEY]._annotated = True
-    meta_custom = node.meta.get("custom", {})
-    meta_custom[ArmAnnotationInfo.CUSTOM_META_KEY] = annotation_info
-    node.meta["custom"] = meta_custom

backends/arm/quantizer/quantization_annotator.py

@@ -394,7 +394,6 @@ def _match_pattern(
     torch.ops.aten.view.default,
     torch.ops.aten.view_as.default,
     torch.ops.aten.view_copy.default,
-    torch.ops.aten._unsafe_view.default,
     torch.ops.aten.select.int,
     torch.ops.aten.select_copy.int,
     torch.ops.aten.slice.Tensor,
@@ -427,7 +426,6 @@ def _match_pattern(
 ]
 
 _one_to_one_shared_input_or_input_act_qspec = [
-    torch.ops.aten.alias.default,
     torch.ops.aten.clone.default,
     torch.ops.aten.hardtanh.default,
     torch.ops.aten.hardtanh_.default,
@@ -695,10 +693,10 @@ def any_or_hardtanh_min_zero(n: Node):
         ]
         quant_properties.quant_output = None
     elif node.target in [
+        torch.ops.aten.scalar_tensor.default,
         torch.ops.aten.full.default,
         torch.ops.aten.full,
         torch.ops.aten.fill_.Scalar,
-        torch.ops.aten.scalar_tensor.default,
     ]:
         quant_properties.quant_inputs = []
         quant_properties.quant_output = _QuantProperty(0, output_act_qspec)

backends/arm/test/misc/test_int64.py

@@ -68,6 +68,10 @@ def forward(self, x: torch.Tensor):
         ConstAdd(torch.int64, 2**40),
         (torch.rand(10) - 0.5,),
     ),
+    "int64_in+float_const": (
+        ConstAdd(torch.float32),
+        (torch.randint(0, 10, (10,)),),
+    ),
     "fp32_in+int64_buffer_chain": (
         BufferChainAdd(torch.int64),
         (torch.rand(2, 5, 3) - 0.5,),
@@ -90,7 +94,7 @@ def test_int64_tosa_FP(test_data: Tuple):
         ArmTester(
             model,
             inputs,
-            common.get_tosa_compile_spec("TOSA-1.0+FP"),
+            common.get_tosa_compile_spec("TOSA-1.0+FP", custom_path="tosa/int64"),
         )
         .export()
         .to_edge_transform_and_lower()

backends/arm/test/models/stable_diffusion/test_SD3Transformer2DModel.py

@@ -39,8 +39,7 @@ class TestSD3Transformer2DModel:
 
     ops_after_partitioner_INT = {
         "executorch_exir_dialects_edge__ops_dim_order_ops__to_dim_order_copy_default": 2,
-        "torch.ops.higher_order.executorch_call_delegate": 3,
-        "executorch_exir_dialects_edge__ops_aten_permute_copy_default": 1,
+        "torch.ops.higher_order.executorch_call_delegate": 2,
     }
 
     def _prepare_inputs(

backends/arm/test/models/test_nn_functional.py

@@ -102,6 +102,7 @@ def test_nn_functional_FP(test_data):
 @parametrize(
     "test_data",
     module_tests,
+    {"normalize": "MLETORCH-1255: Unsupported dtype in InsertTableOpsPass"},
 )
 def test_nn_functional_INT(test_data):
     module, inputs = test_data

backends/arm/test/models/test_torch_functions.py

@@ -126,12 +126,10 @@ def test_torch_fns_FP(test_data):
     xfails={
         "nonzero": "torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode: Could not guard on data-dependent expression Eq(u4, 0). "
         "Requires dynamic output shape.",
-        "eye": "ValueError: Failed processing buffer placeholder: aten_arange_start_step_1_pre_computed_common. "
-        "Is the original torch function supported?",
         "topk": "NotImplementedError: No registered serialization name for <class 'torch.return_types.topk'> found",
         "sort": "NotImplementedError: No registered serialization name for <class 'torch.return_types.sort'> found",
     },
-    strict=True,
+    strict=False,
 )
 def test_torch_fns_INT(test_data):
     module, inputs = test_data

backends/arm/test/ops/test_eye.py

@@ -95,7 +95,7 @@ def test_eye_u85_INT(test_data: test_data_t):
         input_data(),
         EyeAdd.aten_op,
         use_to_edge_transform_and_lower=True,
-    )
+    ).dump_artifact("to_edge_transform_and_lower")
     pipeline.pop_stage("check.quant_nodes")
     pipeline.run()