coremltools 6.2

Core ML Tools 6.2 Release Note

• Support new PyTorch version: torch==1.13.1 and torchvision==0.14.1.
• New ops support:
  • New PyTorch ops support: 1-D and N-D FFT / RFFT / IFFT / IRFFT in torch.fft, torchvision.ops.nms, torch.atan2, torch.bitwise_and, torch.numel,
  • New TensorFlow ops support: FFT / RFFT / IFFT / IRFFT in tf.signal, tf.tensor_scatter_nd_add.
• Existing ops improvements:
  • Supports int input for clamp op.
  • Supports dynamic topk (k not determined during compile time).
  • Supports padding='valid' in PyTorch convolution.
  • Supports PyTorch Adaptive Pooling.
• Supports numpy v1.24.0 (#1718)
• Add int8 affine quantization for the compression_utils.
• Various other bug fixes, optimizations and improvements.

Special thanks to our external contributors for this release: @fukatani, @ChinChangYang, @danvargg, @bhushan23 and @cjblocker.

coremltools 6.1

• Support for TensorFlow 2.10.
• New PyTorch ops supported: baddbmm, glu, hstack, remainder, weight_norm, hann_window, randint, cross, trace, and reshape_as.
• Avoid root logger and use the coremltools logger instead.
• Support dynamic input shapes for PyTorch repeat and expand op.
• Enhance translation of torch where op with only one input.
• Add support for PyTorch einsum equation: 'bhcq,bhck→bhqk’.
• Optimization graph pass improvement
  • 3D convolution batchnorm fusion
  • Consecutive relu fusion
  • Noop elimination
• Actively catch the tensor which has rank >= 6 and error out
• Various other bug fixes, optimizations and improvements.

Special thanks to our external contributors for this release: @fukatani, @piraka9011, @giorgiop, @hollance, @SangamSwadiK, @RobertRiachi, @waylybaye, @GaganNarula, and @sunnypurewal.

coremltools 6.0

• MLProgram compression: affine quantization, palettize, sparsify. See coremltools.compression_utils
• Python 3.10 support.
• Support for latest scikit-learn version (1.1.2).
• Support for latest PyTorch version (1.12.1).
• Support for TensorFlow 2.8.
• Support for options to specify input and output data types, for both images and multiarrays
  • Update coremltools python bindings to work with GRAYSCALE_FLOAT16 image datatype of CoreML
  • New options to set input and output types to multi array of type float16, grayscale image of type float16 and set output type as images, similar to the coremltools.ImageType used with inputs.
• New compute unit enum type: CPU_AND_NE to select the model runtime to the Neural engine and CPU.
• Support for several new TensorFlow and PyTorch ops.
• Changes to opset (available from iOS16, macOS13)
  • New MIL ops: full_like, resample, reshape_like, pixel_unshuffle, topk
  • Existing MIL ops with new functionality: crop_resize, gather, gather_nd, topk, upsample_bilinear.
• API Breaking Changes:
  • Do not assume source prediction column is “predictions”, fixes #58.
  • Remove useCPUOnly parameter from coremltools.convert and coremltools.models.MLModel. Use coremltools.ComputeUnit instead.
  • Remove ONNX support.
  • Remove multi-backend Keras support.
• Various other bug fixes, optimizations and improvements.

coremltools 6.0b2

• Support for new MIL ops added in iOS16/macOS13: pixel_unshuffle, resample, topk
• Update coremltools python bindings to work with GRAYSCALE_FLOAT16 image datatype of CoreML
• New compute unit enum type: CPU_AND_NE
• New PyTorch ops: AdaptiveAvgPool2d, cosine_similarity, eq, linalg.norm, linalg.matrix_norm, linalg.vector_norm, ne, PixelUnshuffle
• Support for identity_n TensorFlow op
• Various other bug fixes, optimizations and improvements.

coremltools 6.0b1

• MLProgram compression: affine quantization, palettize, sparsify. See coremltools.compression_utils.
• New options to set input and output types to multi array of type float16, grayscale image of type float16 and set output type as images, similar to the coremltools.ImageType used with inputs.
• Support for PyTorch 1.11.0.
• Support for TensorFlow 2.8.
• [API Breaking Change] Remove useCPUOnly parameter from coremltools.convert and coremltools.models.MLModel. Use coremltools.ComputeUnit instead.
• Support for many new PyTorch and TensorFlow layers
• Many bug fixes and enhancements.

Known issues

• While conversion and CoreML models with Grayscale Float16 images should work with ios16/macos13 beta, the coremltools-CoreML python binding has an issue which would cause the predict API in coremltools to crash when the either the input or output is of type grayscale float16
• The new Compute unit configuration MLComputeUnitsCPUAndNeuralEngine is not available in coremltools yet

coremltools 5.2

• Support latest version (1.10.2) of PyTorch
• Support TensorFlow 2.6.2
• Support New PyTorch ops:
  • bitwise_not
  • dim
  • dot
  • eye
  • fill
  • hardswish
  • linspace
  • mv
  • new_full
  • new_zeros
  • rrelu
  • selu
• Support TensorFlow ops
  • DivNoNan
  • Log1p
  • SparseSoftmaxCrossEntropyWithLogits
• Various bug fixes, clean ups and optimizations.
• This is the final coremltools version to support Python 3.5

coremltools 5.1

• New supported PyTorch operations: broadcast_tensors, frobenius_norm, full, norm and scatter_add.
• Automatic support for inplace PyTorch operations if non-inplace operation is supported.
• Support PyTorch 1.9.1
• Various other bug fixes, optimizations and improvements.

coremltools 5.0

What’s New

• Added a new kind of Core ML model type, called ML Program. TensorFlow and Pytorch models can now be converted to ML Programs.
  • To learn about ML Programs, how they are different from the classicial Core ML neural network types, and what they offer, please see the documentation here
  • Use the convert_to argument with the unified converter API to indicate the model type of the Core ML model.
    • coremltools.convert(..., convert_to=“mlprogram”) converts to a Core ML model of type ML program.
    • coremltools.convert(..., convert_to=“neuralnetwork”) converts to a Core ML model of type neural network. “Neural network” is the older Core ML format and continues to be supported. Using just coremltools.convert(...) will default to produce a neural network Core ML model.
  • When targeting ML program, there is an additional option available to set the compute precision of the Core ML model to either float 32 or float16. The default is float16. Usage example:
    • ct.convert(..., convert_to=“mlprogram”, compute_precision=ct.precision.FLOAT32) or ct.convert(..., convert_to=“mlprogram”, compute_precision=ct.precision.FLOAT16)
    • To know more about how this affects the runtime, see the documentation on Typed execution.
• You can save to the new Model Package format through the usual coremltool’s save method. Simply use model.save("<model_name>.mlpackage") instead of the usual model.save(<"model_name>.mlmodel")
  • Core ML is introducing a new model format called model packages. It’s a container that stores each of a model’s components in its own file, separating out its architecture, weights, and metadata. By separating these components, model packages allow you to easily edit metadata and track changes with source control. They also compile more efficiently, and provide more flexibility for tools which read and write models.
  • ML Programs can only be saved in the model package format.
• Adds the compute_units parameter to MLModel and coremltools.convert. This matches the MLComputeUnits in Swift and Objective-C. Use this parameter to specify where your models can run:
  • ALL - use all compute units available, including the neural engine.
  • CPU_ONLY - limit the model to only use the CPU.
  • CPU_AND_GPU - use both the CPU and GPU, but not the neural engine.
• Python 3.9 Support
• Native M1 support for Python 3.8 and 3.9
• Support for TensorFlow 2.5
• Support Torch 1.9.0
• New Torch ops: affine_grid_generator, einsum, expand, grid_sampler, GRU, linear, index_put maximum, minimum, SiLUs, sort, torch_tensor_assign, zeros_like.
• Added flag to skip loading a model during conversion. Useful when converting for new macOS on older macOS:
  ct.convert(....., skip_model_load=True)
• Various bug fixes, optimizations and additional testing.

Deprecations and Removals

• Caffe converter has been removed. If you are still using the Caffe converter, please use coremltools 4.
• Keras.io and ONNX converters will be deprecated in coremltools 6. Users are recommended to transition to the TensorFlow/PyTorch conversion via the unified converter API.
• Methods, such as convert_neural_network_weights_to_fp16(), convert_neural_network_spec_weights_to_fp16() , that had been deprecated in coremltools 4, have been removed.
• The useCPUOnly parameter for MLModel and MLModel.predicthas been deprecated. Instead, use the compute_units parameter for MLModel and coremltools.convert.

coremltools 5.0b5

• Added support for pytorch conversion for tensor assignment statements: torch_tensor_assign op and index_put_ op . Fixed bugs in translation of expand ops and sort ops.
• Model input/output name sanitization: input and output names for "neuralnetwork" backend are sanitized (updated to match regex [a-zA-Z_][a-zA-Z0-9_]*), similar to the "mlprogram" backend. So instead of producing input/output names such as "1" or "input/1", "var_1" or "input_1", names will be produced by the unified converter API.
• Fixed a bug preventing a Model Package from being saved more than once to the same path.
• Various bug fixes, optimizations and additional testing.

coremltools 5.0b4

• Fixes Python 3.5 and 3.6 errors when importing some specific submodules.
• Fixes Python 3.9 import error for arm64. #1288