Merge branch 'main' into gh/trivedivivek/42/orig

Author: trivedivivek

.ci/scripts/build-qnn-sdk.sh

@@ -11,7 +11,7 @@ set -o xtrace
 build_qnn_backend() {
   echo "Start building qnn backend."
   export ANDROID_NDK_ROOT=/opt/ndk
-  export QNN_SDK_ROOT=/tmp/qnn/2.25.0.240728
+  export QNN_SDK_ROOT=/tmp/qnn/2.28.0.241029
   export EXECUTORCH_ROOT="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")/../.." && pwd)"
 
   bash backends/qualcomm/scripts/build.sh --skip_aarch64 --job_number 2 --release

.ci/scripts/setup-qnn-deps.sh

@@ -16,9 +16,9 @@ install_qnn() {
   QNN_INSTALLATION_DIR=/tmp/qnn
   mkdir -p "${QNN_INSTALLATION_DIR}"
 
-  curl -Lo /tmp/v2.25.0.24.07.28.zip "https://softwarecenter.qualcomm.com/api/download/software/qualcomm_neural_processing_sdk/v2.25.0.240728.zip"
+  curl -Lo /tmp/v2.28.0.24.10.29.zip "https://softwarecenter.qualcomm.com/api/download/software/qualcomm_neural_processing_sdk/v2.28.0.241029.zip"
   echo "Finishing downloading qnn sdk."
-  unzip -qo /tmp/v2.25.0.24.07.28.zip -d /tmp
+  unzip -qo /tmp/v2.28.0.24.10.29.zip -d /tmp
   echo "Finishing unzip qnn sdk."
 
 

.ci/scripts/test_llama.sh

@@ -121,7 +121,7 @@ echo "COREML option ${COREML}"
 if [[ "${MODE}" =~ .*qnn.* ]]; then
   QNN=ON
   export EXECUTORCH_ROOT="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")/.." && pwd)"
-  export QNN_SDK_ROOT=/tmp/qnn/2.25.0.240728
+  export QNN_SDK_ROOT=/tmp/qnn/2.28.0.241029
   export LD_LIBRARY_PATH="${QNN_SDK_ROOT}/lib/x86_64-linux-clang"
   export PYTHONPATH=".."
   cp schema/program.fbs exir/_serialize/program.fbs

backends/arm/_passes/arm_pass_manager.py

@@ -28,6 +28,7 @@
 )
 from executorch.backends.arm._passes.decompose_linear_pass import DecomposeLinearPass
 from executorch.backends.arm._passes.decompose_meandim_pass import DecomposeMeanDimPass
+from executorch.backends.arm._passes.decompose_select import DecomposeSelectPass
 from executorch.backends.arm._passes.decompose_softmaxes_pass import (
     DecomposeSoftmaxesPass,
 )
@@ -62,7 +63,6 @@
 )
 from executorch.backends.xnnpack._passes.remove_getitem_op import RemoveGetItemPass
 from executorch.exir import ExportedProgram
-from executorch.exir.backend.compile_spec_schema import CompileSpec
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_manager import PassManager
 
@@ -72,9 +72,7 @@ class ArmPassManager(PassManager):
     def _transform(self, graph_module: torch.fx.GraphModule):
         return self(graph_module).graph_module
 
-    def transform_to_backend_pipeline(
-        self, exported_program: ExportedProgram, compile_spec: list[CompileSpec]
-    ):
+    def transform_to_backend_pipeline(self, exported_program: ExportedProgram):
         """Apply passes before transforming program to backend"""
         self.add_pass(FuseQuantizedActivationPass())
         self.add_pass(DecomposeLinearPass())
@@ -137,11 +135,8 @@ def transform_to_backend_pipeline(
         self.add_pass(KeepDimsFalseToSqueezePass())
         self.add_pass(Conv1dUnsqueezePass(exported_program))
         self.add_pass(DecomposeSoftmaxesPass())
-        for spec in compile_spec:
-            if spec.key == "permute_memory_format":
-                memory_format = spec.value.decode()
-                if memory_format == "nhwc":
-                    self.add_pass(AnnotateChannelsLastDimOrder())
+        self.add_pass(DecomposeSelectPass())
+        self.add_pass(AnnotateChannelsLastDimOrder())
 
         return self._transform(exported_program.graph_module)
 

backends/arm/_passes/decompose_select.py

@@ -0,0 +1,56 @@
+# Copyright 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.
+
+# pyre-unsafe
+
+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 ExportPass, PassResult
+
+
+class DecomposeSelectPass(ExportPass):
+    """
+    This pass decomposes select into slice + squeeze to ensure that Aten and TOSA outputs has the same rank (input rank -1)
+    """
+
+    def call(self, graph_module: torch.fx.GraphModule):
+        for node in graph_module.graph.nodes:
+
+            if node.op != "call_function":
+                continue
+
+            if node.target in (
+                exir_ops.edge.aten.select.int,
+                exir_ops.edge.aten.select_copy.int,
+            ):
+                slice_op = exir_ops.edge.aten.slice_copy.Tensor
+                squeeze_op = exir_ops.edge.aten.squeeze_copy.dims
+            else:
+                continue
+
+            input_node, dim, index = node.args
+
+            rank = len(input_node.meta["val"].size())
+            dim = dim % rank if dim < 0 else dim
+            index = index % rank if index < 0 else index
+            dim_list = list(range(rank))
+
+            with graph_module.graph.inserting_before(node):
+                slice_node = create_node(
+                    graph_module.graph, slice_op, (input_node, dim, index, index + 1)
+                )
+                squeeze_node = create_node(
+                    graph_module.graph, squeeze_op, (slice_node, dim_list)
+                )
+
+            node.replace_all_uses_with(squeeze_node)
+            graph_module.graph.erase_node(node)
+
+        graph_module.graph.eliminate_dead_code()
+        graph_module.recompile()
+        graph_module = super().call(graph_module).graph_module
+        return PassResult(graph_module, True)

backends/arm/arm_backend.py

@@ -1,4 +1,4 @@
-# Copyright 2023-2024 Arm Limited and/or its affiliates.
+# Copyright 2023-2025 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.
@@ -49,8 +49,6 @@ def __init__(self):
         self.compiler_flags = []
         self.output_format = None
         self.path_for_intermediates = None
-        # TODO MLETORCH-265 Remove permute_nhwc flag
-        self.permute_nhwc = False
         self.quantize_io = False
         self.tosa_version = None
         self.input_order = None
@@ -118,16 +116,6 @@ def dump_intermediate_artifacts_to(
         self.path_for_intermediates = output_path
         return self
 
-    def set_permute_memory_format(
-        self, set_nhwc_permutation: bool = True
-    ) -> "ArmCompileSpecBuilder":
-        """
-        Permute to channel last in compiler and runtime. Compilation and
-        runtime will convert rank 4 inputs to channel last for each sub-graph.
-        """
-        self.permute_nhwc = set_nhwc_permutation
-        return self
-
     def set_quantize_io(self, quantize_io: bool = False) -> "ArmCompileSpecBuilder":
         """
         Quantization of inputs and dequantization of outputs for cases where
@@ -170,11 +158,6 @@ def build(self) -> List[CompileSpec]:
                 CompileSpec("debug_artifact_path", self.path_for_intermediates.encode())
             )
 
-        if self.permute_nhwc:
-            self.compile_spec.append(
-                CompileSpec("permute_memory_format", "nhwc".encode())
-            )
-
         if self.input_order:
             self.compile_spec.append(
                 CompileSpec(
@@ -188,13 +171,6 @@ def build(self) -> List[CompileSpec]:
         return self.compile_spec
 
 
-def is_permute_memory(compile_spec: List[CompileSpec]) -> bool:
-    for spec in compile_spec:
-        if spec.key == "permute_memory_format":
-            return spec.value.decode() == "nhwc"
-    return False
-
-
 def is_tosa(compile_spec: List[CompileSpec]) -> bool:
     for spec in compile_spec:
         if spec.key == "output_format":
@@ -264,7 +240,7 @@ def preprocess(  # noqa: C901
         # const data directly. Path created and data written only in debug builds.
         tosa_graph = ts.TosaSerializer(artifact_path)
         graph_module = ArmPassManager().transform_to_backend_pipeline(
-            exported_program=edge_program, compile_spec=compile_spec
+            exported_program=edge_program
         )
 
         node_visitors = get_node_visitors(edge_program, tosa_spec)

backends/arm/operators/__init__.py

@@ -30,7 +30,6 @@
     op_repeat,
     op_rshift,
     op_rsqrt,
-    op_select,
     op_sigmoid,
     op_slice,
     op_squeeze,

backends/arm/operators/op_select.py

@@ -1,5 +1,5 @@
 /*
- * Copyright 2023-2024 Arm Limited and/or its affiliates.
+ * Copyright 2023-2025 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.
@@ -76,7 +76,6 @@ namespace arm {
 
 typedef struct {
   FreeableBuffer* processed;
-  bool permuted_io_flag;
 } ExecutionHandle;
 
 extern "C" {
@@ -125,14 +124,6 @@ class ArmBackend final : public ::executorch::runtime::BackendInterface {
         ET_ALLOCATE_INSTANCE_OR_RETURN_ERROR(allocator, ExecutionHandle);
     handle->processed = processed;
 
-    handle->permuted_io_flag = false;
-    for (auto& compile_spec : compile_specs) {
-      if (0 == std::strcmp(compile_spec.key, "permute_memory_format") &&
-          0 == std::memcmp(compile_spec.value.buffer, "nhwc", 4)) {
-        handle->permuted_io_flag = true;
-      }
-    }
-
     // Return the same buffer we were passed - this data will be
     // executed directly
     return handle;
@@ -225,11 +216,7 @@ class ArmBackend final : public ::executorch::runtime::BackendInterface {
       // which require permutation.
       bool permuted_input_shape;
       ET_CHECK_OK_OR_RETURN_ERROR(check_requires_permute(
-          i,
-          tensor_in,
-          &handles.inputs->io[i],
-          execution_handle->permuted_io_flag,
-          &permuted_input_shape));
+          i, tensor_in, &handles.inputs->io[i], &permuted_input_shape));
       bool both_char = tensor_in.scalar_type() == ScalarType::Char and
           handles.inputs->io[i].elem_size == 1;
       bool both_int = tensor_in.scalar_type() == ScalarType::Int and
@@ -330,11 +317,7 @@ class ArmBackend final : public ::executorch::runtime::BackendInterface {
 
       bool permuted_output_shape;
       ET_CHECK_OK_OR_RETURN_ERROR(check_requires_permute(
-          i,
-          tensor_out,
-          &handles.outputs->io[i],
-          execution_handle->permuted_io_flag,
-          &permuted_output_shape));
+          i, tensor_out, &handles.outputs->io[i], &permuted_output_shape));
       if (tensor_out.scalar_type() == ScalarType::Char and
           permuted_output_shape) {
         EXECUTORCH_PROF_SCOPE(
@@ -395,7 +378,6 @@ class ArmBackend final : public ::executorch::runtime::BackendInterface {
       int index,
       const executorch::aten::Tensor tensor,
       VelaIO* io,
-      bool permuted_io_flag,
       bool* is_permuted) const {
     bool permuted_shape = false;
 
@@ -409,12 +391,6 @@ class ArmBackend final : public ::executorch::runtime::BackendInterface {
       if (permuted_shape) {
         ET_LOG(Debug, "Tensor input/output %d will be permuted", index);
       }
-      if (permuted_io_flag != permuted_shape) {
-        ET_LOG(
-            Error,
-            "Permute compile flag and permuted input/output don't agree");
-        return Error::InvalidProgram;
-      }
     }
     *is_permuted = permuted_shape;
     return Error::Ok;