2025-05-09 nightly release (6759d35)

Author: pytorchbot

.ci/docker/ci_commit_pins/buck2.txt

@@ -1 +1 @@
-2024-12-16
+2025-05-06

.github/workflows/build-presets.yml

@@ -11,3 +11,20 @@ on:
 concurrency:
   group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
   cancel-in-progress: true
+
+jobs:
+  apple:
+    uses: pytorch/test-infra/.github/workflows/macos_job.yml@main
+    strategy:
+      matrix:
+        preset: [macos-arm64]
+    with:
+      job-name: build
+      runner: macos-latest-xlarge
+      python-version: 3.12
+      submodules: recursive
+      script: |
+        set -eux
+        ${CONDA_RUN} ./install_requirements.sh > /dev/null
+        ${CONDA_RUN} cmake --preset ${{ matrix.preset }}
+        ${CONDA_RUN} cmake --build cmake-out --parallel

.github/workflows/pull.yml

@@ -434,9 +434,7 @@ jobs:
         output=$(ls -la cmake-out/test/size_test)
         arr=($output)
         size=${arr[4]}
-        # threshold=48120 on devserver with gcc11.4
-        # todo(lfq): update once binary size is below 50kb.
-        threshold="47552"
+        threshold="47560"
         if [[ "$size" -le "$threshold" ]]; then
           echo "Success $size <= $threshold"
         else

CMakeLists.txt

@@ -48,8 +48,13 @@ project(executorch)
 # MARK: - Start EXECUTORCH_H12025_BUILD_MIGRATION --------------------------------------------------
 
 include(${PROJECT_SOURCE_DIR}/tools/cmake/common/preset.cmake)
+
+load_build_preset()
 include(${PROJECT_SOURCE_DIR}/tools/cmake/preset/default.cmake)
 
+# Print all the configs that were called with announce_configured_options.
+print_configured_options()
+
 # MARK: - End EXECUTORCH_H12025_BUILD_MIGRATION ----------------------------------------------------
 
 include(tools/cmake/Utils.cmake)
@@ -175,8 +180,6 @@ option(EXECUTORCH_BUILD_ARM_BAREMETAL
        "Build the Arm Baremetal flow for Cortex-M and Ethos-U" OFF
 )
 
-option(EXECUTORCH_BUILD_COREML "Build the Core ML backend" OFF)
-
 option(EXECUTORCH_BUILD_KERNELS_CUSTOM "Build the custom kernels" OFF)
 
 option(EXECUTORCH_BUILD_KERNELS_CUSTOM_AOT "Build the custom ops lib for AOT"

CMakePresets.json

@@ -0,0 +1,33 @@
+{
+  "version": 10,
+  "cmakeMinimumRequired": {
+    "major": 3,
+    "minor": 31,
+    "patch": 0
+  },
+  "$comment": "On-device AI across mobile, embedded and edge for PyTorch.",
+  "configurePresets": [
+    {
+      "name": "common",
+      "hidden": true,
+      "binaryDir": "${sourceDir}/cmake-out",
+      "generator": "Unix Makefiles"
+    },
+    {
+      "name": "macos-arm64",
+      "inherits": ["common"],
+      "generator": "Xcode",
+      "cacheVariables": {
+        "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/third-party/ios-cmake/ios.toolchain.cmake",
+        "EXECUTORCH_BUILD_PRESET_FILE": "${sourceDir}/tools/cmake/preset/macos-arm64.cmake",
+        "PLATFORM": "MAC_ARM64",
+        "DEPLOYMENT_TARGET": "10.15"
+      },
+      "condition": {
+        "lhs": "${hostSystemName}",
+        "type": "equals",
+        "rhs": "Darwin"
+      }
+    }
+  ]
+}

backends/arm/operator_support/pool_2d_support.py

@@ -54,8 +54,11 @@ def is_node_tosa_supported(self, node: fx.Node, tosa_spec: TosaSpecification):
         kernel = cast(tuple[int, int], node.args[1])
         stride = cast(tuple[int, int], node.args[2])
         if len(node.args) > 3:
+            padding = cast(tuple[int, int], node.args[3])
             # Padding case
-            if not all(1 <= k <= 8 for k in kernel):
+            if not all(1 <= k <= 8 for k in kernel) and not all(
+                v == 0 for v in padding
+            ):
                 self.reporter.report_reject(
                     node, f"Avgpool2d with padding needs kernel dims < 8, got {kernel}"
                 )

backends/arm/operators/op_permute.py

@@ -46,24 +46,26 @@ def permutation_matrix_to_vector(permutation_matrix: torch.Tensor) -> list[int]:
     (1,0,2)
     """
     N = len(permutation_matrix)
-    assert N == len(
-        permutation_matrix[0]
-    ), f"A permutation matrix must be square, got shape {permutation_matrix.shape}"
+    if N != len(permutation_matrix[0]):
+        raise ValueError(
+            f"A permutation matrix must be square, got shape {permutation_matrix.shape}"
+        )
 
     p = [0] * N
     for row_index, row in enumerate(permutation_matrix):
         saw_one = False
         for col_index, value in enumerate(row):
             if value == 1:
-                assert (
-                    not saw_one
-                ), f"A permutation matrix can only have one 1 per row, got row {row}."
+                if saw_one:
+                    raise ValueError(
+                        f"A permutation matrix can only have one 1 per row, got {row=}"
+                    )
                 p[row_index] = col_index
                 saw_one = True
-            else:
-                assert (
-                    value == 0
-                ), f"A permutation matrix only contains 1's and 0's, got value {value}."
+            elif value != 0:
+                raise ValueError(
+                    f"A permutation matrix only contains 1's and 0's, got {value=}"
+                )
     return p
 
 

backends/arm/operators/op_where.py

@@ -69,8 +69,6 @@ def define_node(
     ) -> None:
         import tosa_tools.v0_80.serializer.tosa_serializer as ts  # type: ignore
 
-        validate_num_inputs(self.target, inputs, 3)
-
         bi_supported_dtypes = [
             ts.DType.INT8,
             ts.DType.INT16,
@@ -99,8 +97,6 @@ def define_node(
     ) -> None:
         import tosa_tools.v0_80.serializer.tosa_serializer as ts  # type: ignore
 
-        validate_num_inputs(self.target, inputs, 3)
-
         mi_supported_dtypes = [
             ts.DType.FP16,
             ts.DType.FP32,
@@ -163,8 +159,6 @@ def define_node(
     ) -> None:
         import serializer.tosa_serializer as ts
 
-        validate_num_inputs(self.target, inputs, 3)
-
         bi_supported_dtypes = [
             ts.DType.INT8,
             ts.DType.INT16,
@@ -193,8 +187,6 @@ def define_node(
     ) -> None:
         import serializer.tosa_serializer as ts
 
-        validate_num_inputs(self.target, inputs, 3)
-
         mi_supported_dtypes = [
             ts.DType.FP16,
             ts.DType.FP32,

backends/arm/scripts/parse_test_names.py

@@ -5,7 +5,15 @@
 from executorch.exir.dialects.edge.spec.utils import SAMPLE_INPUT
 
 # Add edge ops which we lower but which are not included in exir/dialects/edge/edge.yaml here.
-CUSTOM_EDGE_OPS = ["linspace.default", "eye.default"]
+CUSTOM_EDGE_OPS = [
+    "linspace.default",
+    "eye.default",
+    "hardsigmoid.default",
+    "hardswish.default",
+    "linear.default",
+    "maximum.default",
+    "adaptive_avg_pool2d.default",
+]
 ALL_EDGE_OPS = SAMPLE_INPUT.keys() | CUSTOM_EDGE_OPS
 
 # Add all targets and TOSA profiles we support here.

backends/arm/test/common.py

@@ -259,17 +259,15 @@ def decorator_func(func):
                     raise RuntimeError(
                         "xfail info needs to be str, or tuple[str, type[Exception]]"
                     )
-                pytest_param = pytest.param(
-                    test_parameters,
-                    id=id,
-                    marks=pytest.mark.xfail(
-                        reason=reason, raises=raises, strict=strict
-                    ),
+                # Set up our fail marker
+                marker = (
+                    pytest.mark.xfail(reason=reason, raises=raises, strict=strict),
                 )
             else:
-                pytest_param = pytest.param(test_parameters, id=id)
-            pytest_testsuite.append(pytest_param)
+                marker = ()
 
+            pytest_param = pytest.param(test_parameters, id=id, marks=marker)
+            pytest_testsuite.append(pytest_param)
         return pytest.mark.parametrize(arg_name, pytest_testsuite)(func)
 
     return decorator_func