Update on "Support named_data in flat_tensor"

Currently flat_tensor ndm only accounts for tensors in get_data, get_num_keys, get_key functions.
Add support to return named_data values as well.

TODO:consolidate tensors and named_data into one structure in the flatbuffer. This will simplify all the serialization and runtime code.

Differential Revision: [D73679683](https://our.internmc.facebook.com/intern/diff/D73679683/)

[ghstack-poisoned]

Author: lucylq

.ci/scripts/test_model.sh

@@ -222,7 +222,7 @@ test_model_with_coreml() {
 
   DTYPE=float16
 
-  "${PYTHON_EXECUTABLE}" -m examples.apple.coreml.scripts.export --model_name="${MODEL_NAME}" --compute_precision "${DTYPE}"
+  "${PYTHON_EXECUTABLE}" -m examples.apple.coreml.scripts.export --model_name="${MODEL_NAME}" --compute_precision "${DTYPE}" --use_partitioner
   EXPORTED_MODEL=$(find "." -type f -name "${MODEL_NAME}*.pte" -print -quit)
 
   if [ -n "$EXPORTED_MODEL" ]; then

Package.swift

@@ -77,7 +77,9 @@ let package = Package(
         name: "\(key)_dependencies",
         dependencies: [.target(name: key)],
         path: ".Package.swift/\(key)",
-        linkerSettings:
+        linkerSettings: [
+          .linkedLibrary("c++")
+        ] +
           (value["frameworks"] as? [String] ?? []).map { .linkedFramework($0) } +
           (value["libraries"] as? [String] ?? []).map { .linkedLibrary($0) }
       ),

backends/apple/coreml/runtime/delegate/coreml_backend_delegate.mm

@@ -88,9 +88,17 @@
         ET_LOG(Error, "%s: DataType=%d is not supported", ETCoreMLStrings.delegateIdentifier.UTF8String, (int)tensor.scalar_type());
         return std::nullopt;
     }
-
+    
     std::vector<ssize_t> strides(tensor.strides().begin(), tensor.strides().end());
     std::vector<size_t> shape(tensor.sizes().begin(), tensor.sizes().end());
+    
+    // If tensor is rank 0, wrap in rank 1
+    // See https://github.com/apple/coremltools/blob/8.2/coremltools/converters/mil/frontend/torch/exir_utils.py#L73
+    if (shape.size() == 0) {
+        shape.push_back(1);
+        strides.push_back(1);
+    }
+    
     MultiArray::MemoryLayout layout(dataType.value(), std::move(shape), std::move(strides));
     switch (argType) {
         case ArgType::Input: {
@@ -233,6 +241,12 @@ ModelLoggingOptions get_logging_options(BackendExecutionContext& context) {
     std::array<SizesType, kTensorDimensionLimit> new_shape;
     for (size_t i = nInputs; i < nInputs + nOutputs; i++) {
         Tensor& t = args[i]->toTensor();
+        // If t has rank 0, do not resize.  delegate_args[i] will have rank 1
+        // because we resized it in get_multi_array
+        if (t.dim() == 0) {
+            continue;
+        }
+
         int rank = delegate_args[i].layout().rank();
         assert (rank <= new_shape.size());
         for (int d = 0; d < rank; d++) {

backends/apple/coreml/runtime/test/ETCoreMLModelManagerTests.mm

@@ -113,7 +113,7 @@ - (void)testAddModelExecution {
     XCTAssertNotNil(inputs);
     MLMultiArray *output = [ETCoreMLTestUtils filledMultiArrayWithShape:inputs[0].shape dataType:inputs[0].dataType repeatedValue:@(0) error:&localError];
     NSArray<MLMultiArray *> *args = [inputs arrayByAddingObject:output];
-    XCTAssertTrue([self.modelManager executeModelWithHandle:handle 
+    XCTAssertTrue([self.modelManager executeModelWithHandle:handle
                                                        args:args
                                              loggingOptions:executorchcoreml::ModelLoggingOptions()
                                                 eventLogger:nullptr

backends/apple/coreml/scripts/install_requirements.sh

@@ -12,7 +12,7 @@ SCRIPT_DIR_PATH="$(
 
 # TODO(jathu): remove the need to fetch coremltools to build deps for coreml_executor_runner.
 # Keep this version in sync with: pyproject.toml
-COREMLTOOLS_VERSION="8.2"
+COREMLTOOLS_VERSION="8.3"
 
 red=`tput setaf 1`
 green=`tput setaf 2`

backends/arm/operators/op_upsample_bilinear2d.py

@@ -34,9 +34,8 @@ def define_node(
         inputs: List[TosaArg],
         output: TosaArg,
     ) -> None:
-        assert (
-            inputs[0].shape is not None and output.shape is not None
-        ), "Only static shapes are supported"
+        if inputs[0].shape is None or output.shape is None:
+            raise ValueError("Only static shapes are supported")
 
         input_dtype = inputs[0].dtype
 
@@ -55,9 +54,12 @@ def define_node(
         def in_int16_range(x):
             return torch.all(x >= -(2**15)) and torch.all(x <= 2**15 - 1)
 
-        assert in_int16_range(scale_n_yx)
-        assert in_int16_range(scale_d_yx)
-        assert in_int16_range(border_yx)
+        if not in_int16_range(scale_n_yx):
+            raise ValueError("scale_n_yx is out of the int16 range")
+        if not in_int16_range(scale_d_yx):
+            raise ValueError("scale_d_yx is out of the int16 range")
+        if not in_int16_range(border_yx):
+            raise ValueError("border_yx is out of the int16 range")
 
         attr = ts.TosaSerializerAttribute()
         attr.ResizeAttribute(

backends/arm/operators/op_upsample_nearest2d.py

@@ -36,9 +36,8 @@ def define_node(
     ) -> None:
         import tosa_tools.v0_80.serializer.tosa_serializer as ts  # type: ignore
 
-        assert (
-            inputs[0].shape is not None and output.shape is not None
-        ), "Only static shapes are supported"
+        if inputs[0].shape is None or output.shape is None:
+            raise ValueError("Only static shapes are supported")
 
         # tosa_shape output is NHWC, take HW
         input_size_yx = torch.tensor(
@@ -55,9 +54,12 @@ def define_node(
         def in_int16_range(x):
             return torch.all(x >= -(2**15)) and torch.all(x <= 2**15 - 1)
 
-        assert in_int16_range(scale_n_yx)
-        assert in_int16_range(scale_d_yx)
-        assert in_int16_range(border_yx)
+        if not in_int16_range(scale_n_yx):
+            raise ValueError("scale_n_yx is out of the int16 range")
+        if not in_int16_range(scale_d_yx):
+            raise ValueError("scale_d_yx is out of the int16 range")
+        if not in_int16_range(border_yx):
+            raise ValueError("border_yx is out of the int16 range")
 
         attr = ts.TosaSerializerAttribute()
         attr.ResizeAttribute(

docs/source/conf.py

@@ -18,9 +18,9 @@
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
+import distutils.file_util
 import glob
 import os
-import shutil
 import sys
 from typing import Any
 
@@ -135,7 +135,7 @@
     # Copy .md files from source dir to gallery dir
     for f in glob.glob(os.path.join(source_dir, "*.md")):
 
-        shutil.copyfile(f, gallery_dir)
+        distutils.file_util.copy_file(f, gallery_dir, update=True)
 
 source_suffix = [".rst", ".md"]
 

docs/source/using-executorch-ios.md

@@ -25,7 +25,7 @@ The prebuilt ExecuTorch runtime, backend, and kernels are available as a [Swift
 
 #### Xcode
 
-In Xcode, go to `File > Add Package Dependencies`. Paste the URL of the [ExecuTorch repo](https://github.com/pytorch/executorch) into the search bar and select it. Make sure to change the branch name to the desired ExecuTorch version in format "swiftpm-<version>", (e.g. "swiftpm-0.6.0"), or a branch name in format "swiftpm-<version>.<year_month_date>" (e.g. "swiftpm-0.7.0-20250401") for a nightly build on a specific date.
+In Xcode, go to `File > Add Package Dependencies`. Paste the URL of the [ExecuTorch repo](https://github.com/pytorch/executorch) into the search bar and select it. Make sure to change the branch name to the desired ExecuTorch version in format "swiftpm-<version>", (e.g. "swiftpm-0.6.0"), or a branch name in format "swiftpm-<version>.<year_month_date>" (e.g. "swiftpm-0.7.0-20250401") for a [nightly build](https://ossci-ios.s3.amazonaws.com/list.html) on a specific date.
 
 ![](_static/img/swiftpm_xcode1.png)