Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
torch.narrow
torch.adaptive_avg_pool1d and torch.adaptive_max_pool1d
torch.numpy_t (i.e. the numpy-style transpose operator .T)torch.clamp_min for integer data typetorch.add for complex data typetf.math.top_k when k is variableThanks to our ExecuTorch partners and our open-source community: @KrassCodes @M-Quadra @teelrabbit @minimalic @alealv @ChinChangYang @pcuenca
New Features:
Includes experimental support for torch.export API but limited to EDGE dialect.
Example usage:
import torch
from torch.export import export
from executorch.exir import to_edge
import coremltools as ct
example_args = (torch.randn(*size), )
aten_dialect = export(AnyNNModule(), example_args)
edge_dialect = to_edge(aten_dialect).exported_program()
edge_dialect._dialect = "EDGE"
mlmodel = ct.convert(edge_dialect)
Enhancements:
ct.utils.make_pipeline - now allows specifying compute_unitsBug Fixes:
Various other bug fixes, enhancements, clean ups and optimizations.
coremltools.optimize for model quantization and compression
coremltools.optimize.coreml for compressing coreml models, in a data free manner. coremltools.compresstion_utils.* APIs have been moved herecoremltools.optimize.torch for compressing torch model with training data and fine-tuning. The fine tuned torch model can then be converted using coremltools.convert
mlprogram for iOS15/macOS12. Previously calling coremltools.convert() without providing the convert_to or the minimum_deployment_target arguments, used the lowest deployment target (iOS11/macOS10.13) and the neuralnetwork backend. Now the conversion process will default to iOS15/macOS12 and the mlprogram backend. You can change this behavior by providing a minimum_deployment_target or convert_to value.repeat_interleave, unflatten, col2im, view_as_real, rand, logical_not, fliplr, quantized_matmul, randn, randn_like, scaled_dot_product_attention, stft, tile
pass_pipeline parameter has been added to coremltools.convert to allow controls over which optimizations are performed..modelc files). Get compiled model files from an MLModel instance. Python API to explicitly compile a model.coremltools.optimize.coreml.get_weights_metadata. This information can be used to customize optimization across ops when using coremltools.optimize.coreml APIs.coremltools.compression_utils is deprecated.mlprogram backend is used.mlprogram:
RangeDim is used and no upper-bound is set (with a positive number), an exception will be raised.inputs parameter but there are undetermined dim in input shape (for example, TF with "None" in input placeholder), it will be sanitized to a finite number (default_size + 1) and raise a warning.Special thanks to our external contributors for this release: @fukatani , @pcuenca , @KWiecko , @comeweber , @sercand , @mlaves, @cclauss, @smpanaro , @nikalra, @jszaday
mlprogram for iOS15/macOS12. Previously calling coremltools.convert() without providing the convert_to or the minimum_deployment_target arguments, used the lowest deployment target (iOS11/macOS10.13) and the neuralnetwork backend. Now the conversion process will default to iOS15/macOS12 and the mlprogram backend. You can change this behavior by providing a minimum_deployment_target or convert_to value.mlprogram backend is used.mlprogram:
RangeDim is used and no upper-bound is set (with a positive number), an exception will be raised.inputs parameter but there are undetermined dim in input shape (for example, TF with "None" in input placeholder), it will be sanitized to a finite number (default_size + 1) and raise a warning.coremltools.optimize.coreml.get_weights_metadata. This information can be used to customize optimization across ops when using coremltools.optimize.coreml APIs.repeat_interleave and unflatten.batch_norm, conv, conv_transpose, expand_dims, gru, instance_norm, inverse, l2_norm, layer_norm, linear, local_response_norm, log, lstm, matmul, reshape_like, resample, resize, reverse, reverse_sequence, rnn, rsqrt, slice_by_index, slice_by_size, sliding_windows, squeeze, transpose.Special thanks to our external contributors for this release: @fukatani, @pcuenca, @KWiecko, @comeweber and @sercand
coremltools.optimize for model quantization and compression
coremltools.optimize.coreml for compressing coreml models, in a data free manner. coremltools.compresstion_utils.* APIs have been moved herecoremltools.optimize.torch for compressing torch model with training data and fine-tuning. The fine tuned torch model can then be converted using coremltools.convert
pass_pipeline parameter has been added to coremltools.convert to allow controls over which optimizations are performed.randn, randn_like, scaled_dot_product_attention, stft, tile
coremltools.models.ml_program.compression_utils is deprecated.Core ML tools 7.0 guide: https://coremltools.readme.io/v7.0/
Special thanks to our external contributors for this release: @fukatani, @pcuenca, @mlaves, @cclauss, @smpanaro, @nikalra, @jszaday
pass_pipeline parameter to coremltools.convert.utils.make_pipeline.converters.mil.debugging_utils.extract_submodel
Special thanks to our external contributors for this release: @fukatani, @nikalra and @kevin-keraudren.
torch==1.13.1 and torchvision==0.14.1.torch.fft, torchvision.ops.nms, torch.atan2, torch.bitwise_and, torch.numel,tf.signal, tf.tensor_scatter_nd_add.clamp op.topk (k not determined during compile time).padding='valid' in PyTorch convolution.Special thanks to our external contributors for this release: @fukatani, @ChinChangYang, @danvargg, @bhushan23 and @cjblocker.
2.10.baddbmm, glu, hstack, remainder, weight_norm, hann_window, randint, cross, trace, and reshape_as.
repeat and expand op.where op with only one input.'bhcq,bhck→bhqk’.Special thanks to our external contributors for this release: @fukatani, @piraka9011, @giorgiop, @hollance, @SangamSwadiK, @RobertRiachi, @waylybaye, @GaganNarula, and @sunnypurewal.
coremltools.compression_utils
1.1.2).1.12.1).2.8.coremltools.ImageType used with inputs.CPU_AND_NE to select the model runtime to the Neural engine and CPU.full_like, resample, reshape_like, pixel_unshuffle, topk
crop_resize, gather, gather_nd, topk, upsample_bilinear.useCPUOnly parameter from coremltools.convert and coremltools.models.MLModel. Use coremltools.ComputeUnit instead.pixel_unshuffle, resample, topk
CPU_AND_NE
AdaptiveAvgPool2d, cosine_similarity, eq, linalg.norm, linalg.matrix_norm, linalg.vector_norm, ne, PixelUnshuffle
identity_n TensorFlow op