Update on "Arm backend: Add 16A8W support and test for sigmoid operation"

Add 16A8W quantization support and test for the sigmoid operation in ExecutorTorch ARM backend.

This follows the pattern established for linear and mul operations, extending int16 support to sigmoid operations.

Changes:
- Add INT16 dtype validation support in op_sigmoid.py
- Add test_sigmoid_tensor_16a8w_tosa_INT test function
- Enable test_sigmoid.py in test targets configuration

The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency.

Differential Revision: [D80510729](https://our.internmc.facebook.com/intern/diff/D80510729/)

cc digantdesai freddan80 per zingo oscarandersson8218

[ghstack-poisoned]

Author: Ninja91

.github/workflows/pull.yml

@@ -971,6 +971,13 @@ jobs:
         ./cmake-out/backends/vulkan/test/custom_ops/q4gsw_linear
         ./cmake-out/backends/vulkan/test/custom_ops/choose_qparams_per_row
 
+        # "Classic" Operator tests
+        PYTHON_EXECUTABLE=python bash backends/vulkan/test/scripts/test_op.sh --build
+        # TODO(ssjia): figure out how to run custom op tests in CI. Currently, they are
+        # failing due to to the libstdc++.so.6 installed with conda not supporting
+        # GLIBCXX_3.4.30. These tests are still run in Meta internal CI.
+        # ./cmake-out/backends/vulkan/test/op_tests/vulkan_sdpa_test
+
         # Run e2e testing for selected operators. More operators will be tested via this
         # route in the future.
         python -m unittest backends/vulkan/test/test_vulkan_delegate.py -k "*pt2e*"

backends/arm/README.md

@@ -206,14 +206,6 @@ The current TOSA version does not support int64. However, int64 is commonly used
 - For quantized models, these transformations will be automatically handled during annotation before the export stage.
 
 List of model specific and optional passes:
-- InsertCastForOpsWithInt64InputPass
-    - Functionality:
-        - For LLMs such as LLama, some opeartors like aten.embedding have int64 input. In order to lower these operators to TOSA, this pass will insert a casting node that converts the input from int64 to int32.
-    - Supported Ops:
-        - aten.embedding.default, aten.slice_copy.Tensor
-    - Example usage:
-        - backends/arm/test/models/test_llama.py
-
 - ConvertInt64ConstOpsToInt32Pass
     - Functionalities:
       - Rewrites constant-producing ops that output int64 to instead output int32, when values are within int32 bounds.
@@ -244,3 +236,16 @@ List of model specific and optional passes:
     - Example usage:
       - (Functionality 1) backends/arm/test/models/stable_diffusion/test_T5EncoderModel.py
       - (Functionality 2) backends/arm/test/models/stable_diffusion/test_CLIPTextModelWithProjection.py
+
+- InsertInt32CastsAfterInt64PlaceholdersPass
+    - Functionalities:
+      - Inserts an int64 -> int32 cast immediately after each int64 placeholder (graph input).
+      - Redirects all uses of each int64 placeholder to its int32 cast output.
+      - Inserts local int32 -> int64 casts at call sites where an operator requires int64 inputs, e.g. `torch.nn.functional.one_hot`
+    - Pass ordering:
+      - When used with `ConvertInt64ConstOpsToInt32Pass` and `ConvertInt64OutputOpsToInt32Pass`, run this pass last.
+      - Rationale: Those passes may cause retracing to re-infer some int64 placeholders as int32. Running this pass last casts only inputs that remain int64, minimizing inserted casts.
+    - Example usage:
+      - backends/arm/test/models/test_llama.py
+      - backends/arm/test/models/stable_diffusion/test_CLIPTextModelWithProjection.py
+      - backends/arm/test/models/stable_diffusion/test_T5EncoderModel.py

backends/arm/_passes/__init__.py

@@ -75,8 +75,8 @@
 from .fuse_constant_ops_pass import ComputeConstantOpsAOT, FuseConstantArgsPass  # noqa
 from .fuse_equal_placeholders_pass import FuseEqualPlaceholdersPass  # noqa
 from .fuse_quantized_activation_pass import FuseQuantizedActivationPass  # noqa
-from .insert_int64_input_cast_pass import (  # noqa  # noqa
-    InsertCastForOpsWithInt64InputPass,
+from .insert_int32_casts_after_int64_placeholders import (  # noqa
+    InsertInt32CastsAfterInt64PlaceholdersPass,
 )
 from .insert_rescales_pass import InsertRescalePass  # noqa
 from .insert_table_ops import InsertTableOpsPass  # noqa

backends/arm/_passes/arm_pass_manager.py

@@ -76,7 +76,7 @@
     FuseConstantArgsPass,
     FuseEqualPlaceholdersPass,
     FuseQuantizedActivationPass,
-    InsertCastForOpsWithInt64InputPass,
+    InsertInt32CastsAfterInt64PlaceholdersPass,
     InsertRescalePass,
     InsertTableOpsPass,
     MatchArgDtypePass,
@@ -277,7 +277,7 @@ def transform_for_annotation_pipeline(self, graph_module: GraphModule):
         )  # ConvertInt64ConstOpsToInt32Pass requires this pass to remove the assertation in Graph
         self.add_pass(ConvertInt64ConstOpsToInt32Pass())
         self.add_pass(ConvertInt64OutputOpsToInt32Pass())
-        self.add_pass(InsertCastForOpsWithInt64InputPass())
+        self.add_pass(InsertInt32CastsAfterInt64PlaceholdersPass())
         self.add_pass(DecomposeEmbeddingPass())
         self.add_pass(DecomposeScaledDotProductAttention())
         self.add_pass(DecomposeRoundPass())

backends/arm/_passes/insert_int32_casts_after_int64_placeholders.py

@@ -0,0 +1,122 @@
+# 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.
+
+# pyre-unsafe
+
+
+import logging
+
+import torch
+from executorch.backends.arm._passes.arm_pass_utils import create_node
+from executorch.exir.dialects._ops import ops as exir_ops
+from executorch.exir.pass_base import EdgeOpOverload, ExportPass, PassResult
+from torch._subclasses.fake_tensor import FakeTensor
+
+
+logger = logging.getLogger(__name__)
+
+
+class InsertInt32CastsAfterInt64PlaceholdersPass(ExportPass):
+    """
+    Insert an int64->int32 cast after each int64 placeholder.
+
+    Note: Overflow checks are not applied in this pass. It is the user's responsibility to ensure that values fit within
+    the int32 range.
+    """
+
+    # Ops that require i64 inputs → positions of args to upcast.
+    # Key: op overload; Value: zero-based indices of positional args that must be i64.
+    I64_INPUT_ARG_POSITIONS = {
+        torch.ops.aten.one_hot.default: (0,),
+    }
+
+    def _insert_callsite_i32_to_i64_casts(self, graph_module: torch.fx.GraphModule):
+        """
+        If an operator requires int64 inputs but dtype propagation (via call_operator)
+        produced int32, insert a local int32→int64 cast at the call site to satisfy
+        PyTorch's operator input validation.
+        """
+        modified = False
+        graph = graph_module.graph
+        for node in graph.nodes:
+            if node.op != "call_function":
+                continue
+            if node.target not in self.I64_INPUT_ARG_POSITIONS:
+                continue
+
+            with graph.inserting_before(node):
+                arg_positions = self.I64_INPUT_ARG_POSITIONS.get(node.target)
+                args_list = list(node.args)
+                for pos in arg_positions:  # type: ignore[union-attr]
+                    input_arg = args_list[pos]
+                    to_copy_op = self._get_decomposition(graph)
+                    cast_node = graph_module.graph.create_node(
+                        "call_function",
+                        to_copy_op,
+                        (input_arg,),
+                        {"dtype": torch.int64},
+                    )
+                    cast_node.meta["val"] = node.meta["val"].to(torch.int64)
+                    args_list[pos] = cast_node
+                node.args = tuple(args_list)
+                modified = True
+        return modified
+
+    def _graph_uses_edge_ops(self, graph: torch.fx.Graph) -> bool:
+        for n in graph.nodes:
+            if n.op == "call_function":
+                if isinstance(n.target, EdgeOpOverload):
+                    return True
+        return False
+
+    def _get_decomposition(self, graph: torch.fx.Graph):
+        if self._graph_uses_edge_ops(graph):
+            return exir_ops.edge.dim_order_ops._to_dim_order_copy.default
+        else:
+            return torch.ops.dim_order_ops._to_dim_order_copy.default
+
+    def _is_tensor_of_dtype(self, node_val, dtype: torch.dtype) -> bool:
+        return isinstance(node_val, FakeTensor) and node_val.dtype == dtype
+
+    def _insert_placeholder_i64_to_i32_casts(self, graph_module: torch.fx.GraphModule):
+        modified = False
+        graph = graph_module.graph
+        for node in graph.nodes:
+            if node.op != "placeholder":
+                continue
+            node_val = node.meta["val"]
+            if not self._is_tensor_of_dtype(node_val, torch.int64):
+                continue
+
+            to_copy_op = self._get_decomposition(graph)
+            with graph.inserting_after(node):
+                cast_after = create_node(
+                    graph,
+                    to_copy_op,
+                    args=(node,),
+                    kwargs={
+                        "dtype": torch.int32,
+                    },
+                )
+                users = [user for user in node.users if user != cast_after]
+                for user in users:
+                    user.replace_input_with(node, cast_after)
+                logger.warning(
+                    f"Inserting a casting node {cast_after.name} after {node.name} to cast int64 placeholder"
+                    f" to int32 for {node.name} defined in {node.meta.get('stack_trace','[no stack trace found]')}"
+                )
+                modified = True
+        return modified
+
+    def call(self, graph_module: torch.fx.GraphModule):
+        modified = False
+        modified |= self._insert_placeholder_i64_to_i32_casts(graph_module)
+        modified |= self._insert_callsite_i32_to_i64_casts(graph_module)
+
+        if modified:
+            graph_module.graph.eliminate_dead_code()
+            graph_module.recompile()
+            graph_module = super().call(graph_module).graph_module
+        return PassResult(graph_module, modified)

backends/arm/_passes/insert_int64_input_cast_pass.py

@@ -73,7 +73,9 @@ def define_node(
             abs_output = output
 
         # Do the INT32 Abs
-        tosa_graph.addOperator(
+        self._serialize_operator(
+            node,
+            tosa_graph,
             ts.TosaOp.Op().ABS,
             [
                 rescaled_inputs[0].name,

backends/arm/operators/op_abs.py

@@ -67,7 +67,9 @@ def define_node(
             dtype=ts.DType.INT32,
         )
 
-        tosa_graph.addOperator(
+        self._serialize_operator(
+            node,
+            tosa_graph,
             ts.TosaOp.Op().REDUCE_SUM,
             [rescaled_inputs[0].name],
             [intermediate.name],
@@ -111,7 +113,9 @@ def define_node(
         attr = ts.TosaSerializerAttribute()
         attr.ReduceSumAttribute(tensor.dim_order.index(dim))
 
-        tosa_graph.addOperator(
+        self._serialize_operator(
+            node,
+            tosa_graph,
             ts.TosaOp.Op().REDUCE_SUM,
             [tensor.name],
             [output.name],

backends/arm/operators/op_sum.py

@@ -44,6 +44,7 @@ python_library(
         "//executorch/backends/arm:ethosu_partitioner",
         "//executorch/backends/arm/quantizer:lib",
         "//executorch/backends/arm/tosa:mapping",
+        "//executorch/backends/arm:vgf_partitioner",
         "//executorch/devtools/backend_debug:delegation_info",
         "//executorch/exir/backend:operator_support",
         "fbsource//third-party/pypi/tabulate:tabulate",

backends/arm/test/TARGETS

@@ -11,7 +11,7 @@
 from executorch.backends.arm._passes import (
     ConvertInt64ConstOpsToInt32Pass,
     ConvertInt64OutputOpsToInt32Pass,
-    InsertCastForOpsWithInt64InputPass,
+    InsertInt32CastsAfterInt64PlaceholdersPass,
 )
 
 from executorch.backends.arm.test import common
@@ -33,10 +33,9 @@ class TestCLIPTextModelWithProjection(unittest.TestCase):
     # for that is some assert ops are removed by passes in the
     # .to_executorch step, i.e. after Arm partitioner.
     ops_after_partitioner = {
-        "executorch_exir_dialects_edge__ops_dim_order_ops__to_dim_order_copy_default": 3,
-        "executorch_exir_dialects_edge__ops_aten_view_copy_default": 1,
         "executorch_exir_dialects_edge__ops_aten_argmax_default": 1,
-        "torch.ops.higher_order.executorch_call_delegate": 1,
+        "executorch_exir_dialects_edge__ops_dim_order_ops__to_dim_order_copy_default": 2,
+        "torch.ops.higher_order.executorch_call_delegate": 2,
     }
 
     def _prepare_inputs(
@@ -71,9 +70,9 @@ def test_CLIPTextModelWithProjection_tosa_FP(self):
                     example_inputs=text_encoder_model_inputs,
                     compile_spec=common.get_tosa_compile_spec(tosa_spec="TOSA-1.0+FP"),
                     transform_passes=[
-                        InsertCastForOpsWithInt64InputPass(),
                         ConvertInt64ConstOpsToInt32Pass(),
                         ConvertInt64OutputOpsToInt32Pass(),
+                        InsertInt32CastsAfterInt64PlaceholdersPass(),
                     ],
                 )
                 .export()