Core ML Backend

Core ML delegate is the ExecuTorch solution to take advantage of Apple’s Core ML framework for on-device ML. With Core ML, a model can run on CPU, GPU, and the Apple Neural Engine (ANE).

Features

• Dynamic dispatch to the CPU, GPU, and ANE.

• Supports fp32 and fp16 computation.

Target Requirements

Below are the minimum OS requirements on various hardware for running a Core ML-delegated ExecuTorch model:

• macOS >= 13.0

• iOS >= 16.0

• iPadOS >= 16.0

• tvOS >= 16.0

Development Requirements

To develop you need:

• macOS >= 13.0

• Xcode >= 14.1

Before starting, make sure you install the Xcode Command Line Tools:

xcode-select --install

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Using the Core ML Backend

To target the Core ML backend during the export and lowering process, pass an instance of the CoreMLPartitioner to to_edge_transform_and_lower. The example below demonstrates this process using the MobileNet V2 model from torchvision.

import torch
import torchvision.models as models
from torchvision.models.mobilenetv2 import MobileNet_V2_Weights
from executorch.backends.apple.coreml.partition import CoreMLPartitioner
from executorch.exir import to_edge_transform_and_lower

mobilenet_v2 = models.mobilenetv2.mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT).eval()
sample_inputs = (torch.randn(1, 3, 224, 224), )

et_program = to_edge_transform_and_lower(
    torch.export.export(mobilenet_v2, sample_inputs),
    partitioner=[CoreMLPartitioner()],
).to_executorch()

with open("mv2_coreml.pte", "wb") as file:
    et_program.write_to_file(file)

See Partitioner API for a reference on available partitioner options.

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Quantization

The Core ML delegate can also be used as a backend to execute quantized models. See Core ML Quantization for more information on available quantization schemes and APIs.

Backward compatibility

Core ML supports backward compatibility via the minimum_deployment_target option. A model exported with a specific deployment target is guaranteed to work on all deployment targets >= the specified deployment target. For example, a model exported with coremltools.target.iOS17 will work on iOS 17 or higher.

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Runtime integration

To run the model on device, use the standard ExecuTorch runtime APIs. See Running on Device for more information, including building the iOS frameworks.

When building from source, pass -DEXECUTORCH_BUILD_COREML=ON when configuring the CMake build to compile the Core ML backend.

Due to the use of static initializers for registration, it may be necessary to use whole-archive to link against the coremldelegate target. This can typically be done by passing "$<LINK_LIBRARY:WHOLE_ARCHIVE,coremldelegate>" to target_link_libraries.

# CMakeLists.txt
add_subdirectory("executorch")
...
target_link_libraries(
    my_target
    PRIVATE executorch
    extension_module_static
    extension_tensor
    optimized_native_cpu_ops_lib
    $<LINK_LIBRARY:WHOLE_ARHIVE,coremldelegate>)

No additional steps are necessary to use the backend beyond linking the target. A Core ML-delegated .pte file will automatically run on the registered backend.

Reference

→Troubleshooting — Debug common issues.

→Partitioner API — Partitioner options.

→Quantization — Supported quantization schemes.

→Op support — Supported operators.