Update base for Update on "[ET-VK] Creating get_symmetric_quantization_config"

# Context

Eventually dynamic quantization will be enabled in the vulkan_quantizer (particularly 8bit dyn act with 8bit weights). In order to enable this functionality we need to utilize a similar method as XNNPack with how they define their quantization config. This diff aims to align with XNNPack quantizer logic and also migrate away from utilizing the old static quantization config logic.

# Changes

A few noticable changes is that we migrate away from `get_linear_weight_only_qcs_xnn_qconfig`, and we now define a symmetric config that has parameters to define whether it's dynamically quantized or not. Furthermore, we also incorporate bits_to_range so that we can automatically designate the min and max quant ranges without having to set them during initialization. We also change some wording from using just static as we are now enabling dynamic quantization as well.

Furthermore, we change internally other codebases that are calling our existing legacy config, and move them into the more universal symmetric config. Since this follows the same naming scheme as XNNPack, I have decided to just add aliases in cases where its being imported directly along with XNNPack.

Differential Revision: [D78291249](https://our.internmc.facebook.com/intern/diff/D78291249/)

[ghstack-poisoned]

Author: morelos

.github/workflows/trunk.yml

@@ -302,6 +302,37 @@ jobs:
           exit 1
         fi
 
+  test-arm-ootb-linux:
+    name: test-arm-ootb-linux
+    uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
+    permissions:
+      id-token: write
+      contents: read
+    with:
+      runner: linux.2xlarge
+      docker-image: executorch-ubuntu-22.04-arm-sdk
+      submodules: 'recursive'
+      ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
+      timeout: 90
+      script: |
+        # The generic Linux job chooses to use base env, not the one setup by the image
+        CONDA_ENV=$(conda env list --json | jq -r ".envs | .[-1]")
+        conda activate "${CONDA_ENV}"
+
+        # Follow the steps required before running the notebooks
+        # Try to mirror these as closely as possible
+        source .ci/scripts/utils.sh
+        install_executorch "--use-pt-pinned-commit"
+
+        .ci/scripts/setup-arm-baremetal-tools.sh
+        source examples/arm/ethos-u-scratch/setup_path.sh
+
+        # Install requirements for converting notebooks
+        pip install notebook
+
+        # Run OOTB tests
+        backends/arm/test/test_arm_ootb.sh
+
   test-coreml-delegate:
     name: test-coreml-delegate
     uses: pytorch/test-infra/.github/workflows/macos_job.yml@main

CMakePresets.json

@@ -15,7 +15,8 @@
         "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/third-party/ios-cmake/ios.toolchain.cmake",
         "EXECUTORCH_BUILD_PRESET_FILE": "${sourceDir}/tools/cmake/preset/macos.cmake",
         "PLATFORM": "MAC_ARM64",
-        "DEPLOYMENT_TARGET": "12.0"
+        "DEPLOYMENT_TARGET": "12.0",
+        "CMAKE_MACOSX_BUNDLE": "OFF"
       },
       "condition": {
         "lhs": "${hostSystemName}",

backends/arm/_passes/decompose_asin_pass.py

@@ -85,12 +85,11 @@ def _build_polynomial(
         return result
 
     def call_operator(self, op, args, kwargs, meta):
+        if op not in edge_asin_op:
+            return super().call_operator(op, args, kwargs, meta)
         logging.info(
             f"Approximating asin. This may introduce small numerical errors. For details, see {__file__}."
         )
-        if op not in edge_asin_op:
-            return super().call_operator(op, args, kwargs, meta)
-
         x = args[0]
         half = 0.5
         one = 1.0

backends/arm/test/ops/test_add.py

@@ -205,6 +205,5 @@ def test_add_tensor_vgf_int(test_data: input_t1):
         aten_op,
         exir_op,
         tosa_version="TOSA-1.0+INT",
-        symmetric_io_quantization=True,
     )
     pipeline.run()

backends/arm/test/test_arm_ootb.sh

@@ -0,0 +1,18 @@
+#!/usr/bin/env bash
+
+# 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.
+
+set -e
+
+run_ootb_tests_ethos_u() {
+    echo "$FUNCNAME: Running out-of-the-box tests for Arm Ethos-U"
+    jupyter nbconvert \
+        --to notebook \
+        --execute examples/arm/ethos_u_minimal_example.ipynb
+    echo "${FUNCNAME}: PASS"
+}
+
+run_ootb_tests_ethos_u

backends/arm/test/tester/arm_tester.py

@@ -43,6 +43,7 @@
     EthosUQuantizer,
     get_symmetric_quantization_config,
     TOSAQuantizer,
+    VgfQuantizer,
 )
 from executorch.backends.arm.test.runner_utils import (
     dbg_tosa_fb_to_json,
@@ -332,6 +333,8 @@ def quantize(
                 quantizer = TOSAQuantizer(tosa_spec)
             elif is_ethosu(self.compile_spec):
                 quantizer = EthosUQuantizer(self.compile_spec)
+            elif is_vgf(self.compile_spec):
+                quantizer = VgfQuantizer(self.compile_spec)
             quantize_stage = tester.Quantize(
                 quantizer,
                 get_symmetric_quantization_config(),

backends/arm/test/tester/test_pipeline.py

@@ -861,18 +861,15 @@ def __init__(
         rtol: float = 1e-03,
         qtol: int = 1,
         dynamic_shapes: Optional[Tuple[Any]] = None,
+        transform_passes: Optional[
+            Union[Sequence[PassType], Dict[str, Sequence[PassType]]]
+        ] = None,
     ):
 
         if (
             symmetric_io_quantization or per_channel_quantization
         ) and tosa_version == "TOSA-1.0+FP":
             raise ValueError("Dont configure quantization with FP TOSA profile.")
-        if (
-            symmetric_io_quantization is False
-            and per_channel_quantization is False
-            and tosa_version == "TOSA-1.0+INT"
-        ):
-            raise ValueError("Missing quantization options for INT TOSA profile.")
 
         tosa_profile = TosaSpecification.create_from_string(tosa_version)
         compile_spec = common.get_vgf_compile_spec(
@@ -887,6 +884,7 @@ def __init__(
             exir_op,
             use_to_edge_transform_and_lower,
             dynamic_shapes,
+            transform_passes=transform_passes,
         )
 
         if symmetric_io_quantization or per_channel_quantization:
@@ -900,7 +898,7 @@ def __init__(
         else:
             quant_stage = None
 
-        if quant_stage:
+        if "INT" in tosa_version:
             self.add_stage(self.tester.quantize, quant_stage, pos=0)
 
             self.add_stage_after(

backends/cadence/aot/TARGETS

@@ -539,8 +539,10 @@ python_unittest(
     ],
     typing = True,
     deps = [
+        ":program_builder",
         "//executorch/backends/cadence/aot:graph_builder",
         "//executorch/backends/cadence/aot:ops_registrations",
+        "//executorch/runtime:runtime",
         "//later:lib",
     ],
 )

backends/cadence/aot/memory_planning.py

@@ -19,7 +19,10 @@
     MemoryPlanningAlgo,
     MemoryPlanningState,
 )
-from executorch.backends.cadence.aot.utils import MemoryConfig
+from executorch.backends.cadence.aot.utils import (
+    MemoryConfig,
+    MemoryPlanningAlgoFailure,
+)
 
 from executorch.exir import ExecutorchProgramManager
 from executorch.exir.memory_planning import collect_specs_from_nodes, Verifier
@@ -95,7 +98,9 @@ def plan(
         ):
             self.plan_spec(spec, state, placement_constraints)
             if not state.is_placed(spec):
-                raise MemoryError(f"Cannot fit {spec} in any memory hierarchy")
+                raise MemoryPlanningAlgoFailure(
+                    f"Cannot fit {spec} {spec.allocated_memory=} in any memory hierarchy for {self.memory_config}"
+                )
 
 
 class GreedyWithHeuristic(MemoryPlanningAlgo):
@@ -169,7 +174,9 @@ def plan(
         ):
             self.plan_spec(spec, state, placement_constraints)
             if not state.is_placed(spec):
-                raise MemoryError(f"Cannot fit {spec} in any memory hierarchy")
+                raise MemoryPlanningAlgoFailure(
+                    f"Cannot fit {spec} in any memory hierarchy for {self.memory_config}"
+                )
 
         logging.debug(
             f"greedy by size for offset calculation with hierarchy returns bufsizes: {state.bufsizes}"

backends/cadence/aot/tests/test_fusion_ops_passes.py

@@ -12,7 +12,6 @@
 
 import executorch.backends.cadence.aot.ops_registrations  # noqa
 import torch
-from executorch.backends.cadence.aot import compiler
 from executorch.backends.cadence.aot.fuse_ops import (
     FuseCascadedTransposeOrPermuteOps,
     FuseCascadedViewOps,
@@ -30,7 +29,6 @@
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.dialects.edge._ops import EdgeOpOverload
 from executorch.exir.pass_base import PassResult, ProxyValue
-from torch import nn
 
 
 class TestFusionPassesBase(unittest.TestCase):
@@ -178,43 +176,6 @@ def test_keep_mm_add_with_multiple_users(self) -> None:
         self.assertEqual(count_node(converted_graph, exir_ops.edge.aten.mm.default), 1)
         self.assertEqual(count_node(converted_graph, exir_ops.edge.aten.add.Tensor), 3)
 
-    # TODO(matthiascremon) -> None: enable that pass with new flow
-    @torch.no_grad()
-    @unittest.expectedFailure
-    def test_legacy_conv_bn_fusion(self) -> None:
-        class ModelConvBN(torch.nn.Module):
-            def __init__(
-                self, in_features: int, out_features: int, kernel_size: int
-            ) -> None:
-                super().__init__()
-                self.conv1d = nn.Conv1d(in_features, out_features, kernel_size)
-                self.bn = nn.BatchNorm1d(out_features)
-
-            def forward(self, x: torch.Tensor) -> torch.Tensor:
-                y = self.conv1d(x)
-                return self.bn(y)
-
-        model = ModelConvBN(64, 1, 2)
-        x = torch.randn(1, 64, 4)
-
-        graph_module = (
-            compiler.export_to_executorch_gen_etrecord(model.eval(), (x,))
-            .exported_program()
-            .graph_module
-        )
-        # Assert that after running the fusion passes, batchnorm was fused with conv1d
-        self.assertEqual(
-            count_node(graph_module, torch.ops.aten.linear.out)
-            + count_node(graph_module, torch.ops.cadence.convolution.out),
-            1,
-        )
-        self.assertEqual(
-            count_node(
-                graph_module, torch.ops.aten._native_batch_norm_legit_no_training.out
-            ),
-            0,
-        )
-
     def test_permute_transpose_fusion(self) -> None:
         builder = GraphBuilder()
         x = builder.placeholder("x", torch.randn(3, 1, 3, 1, 4, dtype=torch.float32))