coremltools - Coremltools 9.0b1

Support for model input/output with int8 dtype
Ability to read and write model state
iOS26/macOS26/watchOS26/tvOS26 deployment targets
AllowLowPrecisionAccumulationOnGPU optimization hint
Support for PyTorch 2.7 and ExecuTorch 0.5
Additional metadata automatically added to converted models
Optimize im2col PyTorch operation.
Various other bug fixes, enhancements, clean ups and optimizations

Special thanks to our external contributors for this release: @pchen7e2, @GameRoMan, @tritolol, @james-p-xu, @M-Quadra, @reneleonhardt, @kasper0406

- Python
Published by TobyRoseman 10 months ago

coremltools - 8.3.0 Release

Features debugging and benchmarking utilities to root cause numerical issues and performance bottlenecks,
- MLModelValidator:
  - Helper utils to assist with finding problematic ops producing NaNs, infinities or any custom validation logic
- MLModelComparator | TorchScriptMLModelComparator | TorchExportMLModelComparator:
  - Makes it easy to compare two CoreML models or a CoreML with a Torch model
  - Particularly useful in triaging numerical discrepancies
    - Between fp16 and fp32 variants of a CoreML model
    - Or, across different compute engines for a CoreML model
- MLModelInspector:
  - Facilities retrieval of intermediates tensors of CoreML model
- MLModelBenchmarker | TorchMLModelBenchmarker:
  - Utils to log model loading time, prediction latency, and the execution times of individual operations
  - Ties it down to PyTorch modules → makes it possible to identify slow PyTorch modules or nodes.
- Remote Device:
  - Utilities to benchmark and/or debug models on connected devices
  - Works seamlessly with its siblings MLModelBenchmarker, MLModelInspector, MLModelValidator, and MLModelComparator etc.
  - Supports iOS, macOS, watchOS, and tvOS devices
Various other bug fixes, enhancements, clean ups and optimizations
- Uint8 support for grayscale images
- Support for additional PyTorch operations: linalg.vecdot, pow, argmin
- Fixes in lowering of batch_norm, ConvTranspose1d, randn
- Improving ANE residency of top-k operation
- Better error handling around deployment targets for PTQ APIs
- Bug fix in weight quantization of fp16 neuralnetwork models
- Fixing registration of compression passes
- Correct handling of feature names during conversion of XGBoost models
Special thanks to 3P Developers: @RGooBS24 , @M-Quadra , @smpanaro , @Zerui18 , @yushangdi , @lkb85 , @Pranaykarvi , @billmguo , @metascroy , @benoit-vinsonneau , @ccyoyou , @reneleonhardt !!

- Python
Published by jakesabathia2 about 1 year ago

coremltools - Release Notes

Increase conversion support coverage for models produced by torch.export
- All common ops have been covered, that is 83% parity with our mature torch.jit.trace converter
- A new model supported: torchaudio wav2vec
A new transformers graph pass "common::scaleddotproductattentionsliced_q" for improved performance of sdpa calculation with long sequence lengths (e.g. Depth-Anything model, with sequence length 1814, transformed by the graph pass was 34% faster and used 45% less memory on ANE)
New torch ops supported
- nativegroupnorm
- bool type triu
Bug fixes
- Incorrect behaviour of torch.linspace
- Wrong power 2 - sqrt fusion when power exponent is a tensor

Special thanks to our open source community contributors for this release: @RGooBS24 @fukatani @twoertwein @kasper0406

- Python
Published by YifanShenSZ over 1 year ago

Release Notes

Python Support
- Compatible with Python 3.12.
Added support for additional PyTorch operations
- torch.clamp_max, torch.rand_like, torch.all, torch.linalg_inv, torch.nan_to_num, torch.cumprod, torch.searchsorted ops are now supported.
Increased conversion support coverage for models produced by torch.export
- Op translation support is at 68% parity with our mature torch.jit.traceconverter.
- Support enumerated shape model.
- Support ImageType input.
Added Python bindings for the following classes:
Various other bug fixes, enhancements, clean ups and optimizations.
- Favor bool mask in scaled dot product attention
- Fix quantization crash with bool mask
- Special thanks to our external contributors for this release: @M-Quadra @benjaminkech @guru-desh

- Python
Published by cymbalrush over 1 year ago

Release Notes

Compare to 7.2 (including features from 8.0b1 and 8.0b2)

Support for Latest Dependencies
- Compatible with the latest protobuf python package which improves serialization latency.
- Support torch 2.4.0, numpy 2.0, scikit-learn 1.5.
Support stateful Core ML models
- Updates to the converter to produce Core ML models with the State Type (new type introduced in iOS18/macOS15).
- Adds a toy stateful attention example model to show how to use in-place kv-cache.
Increase conversion support coverage for models produced by torch.export
- Op translation support is at 56% parity with our mature torch.jit.trace converter
- Representative deep learning models (mobilebert, deeplab, edsr, mobilenet, vit, inception, resnet, wav2letter, emformer) have been supported
- Representative foundation models (llama, stable diffusion) have been supported
- The model quantized by ct.optimize.torch could be exported by torch.export and then convert.
New Compression Features
- coremltools.optimize
  - Support compression with more granularities: blockwise quantization, grouped channel wise palettization
  - 4 bit weight quantization and 3 bit palettization
  - Support joint compression modes (8 bit look-up-tables for palettization, pruning+quantization/palettization)
  - Vector palettization by setting cluster_dim > 1 and palettization with per channel scale by setting enable_per_channel_scale=True.
  - Experimental activation quantization (take a W16A16 Core ML model and produce a W8A8 model)
  - API updates for coremltools.optimize.coreml and coremltools.optimize.torch
- Support some models quantized by torchao (including the ops produced by torchao such as _weight_int4pack_mm).
- Support more ops in quantized_decomposed namespace, such as embedding_4bit, etc.
Support new ops and fixes bugs for old ops
- compression related ops: constexpr_blockwise_shift_scale, constexpr_lut_to_dense, constexpr_sparse_to_dense, etc
- updates to the GRU op
- SDPA op scaled_dot_product_attention
- clip op
Updated the model loading API
- Support optimizationHints.
- Support loading specific functions for prediction.
New utilities in coremltools.utils
- coremltools.utils.MultiFunctionDescriptor and coremltools.utils.save_multifunction, for creating an mlprogram with multiple functions in it, that can share weights.
- coremltools.models.utils.bisect_model can break a large Core ML model into two smaller models with similar sizes.
- coremltools.models.utils.materialize_dynamic_shape_mlmodel can convert a flexible input shape model into a static input shape model.
Various other bug fixes, enhancements, clean ups and optimizations
Special thanks to our external contributors for this release: @sslcandoit @FL33TW00D @dpanshu @timsneath @kasper0406 @lamtrinhdev @valfrom @teelrabbit @igeni @Cyanosite

- Python
Published by junpeiz over 1 year ago

coremltools - coremltools 8.0b2

Release Notes

Support for Latest Dependencies
- Compatible with the latest protobuf python package: Improves serialization latency.
- Compatible with numpy 2.0.
- Supports scikit-learn 1.5.
New Core ML model utils
- coremltools.models.utils.bisect_model can break a large Core ML model into two smaller models with similar sizes.
- coremltools.models.utils.materialize_dynamic_shape_mlmodel can convert a flexible input shape model into a static input shape model.
New compression features in coremltools.optimize.coreml
- Vector palettization: By setting cluster_dim > 1 in coremltools.optimize.coreml.OpPalettizerConfig, you can do the vector palettization, where each entry in the lookup table is a vector of length cluster_dim.
- Palettization of per channel scale: By setting enable_per_channel_scale=True in coremltools.optimize.coreml.OpPalettizerConfig, weights are normalized along the output channel using per channel scales before being palettized.
- Joint compression: A new pattern is supported, where weights are first quantized to int8 and then palettized into n-bit look-up table with int8 entries.
- Support conversion of palettized model with 8bits LUT produced from coremltools.optimize.torch.
New compression features / bug fixes in coremltools.optimize.torch
- Added conversion support for Torch models jointly compressed using the training time APIs in coremltools.optimize.torch .
- Added vector palettization support to SKMPalettizer .
- Fixed bug in construction of weight vectors along output channel for vector palettization with PostTrainingPalettizer and DKMPalettizer .
- Deprecated cluter_dtype option in favor of lut_dtype in ModuleDKMPalettizerConfig .
- Added support for quantizing ConvTranspose modules with PostTrainingQuantizer and LinearQuantizer .
- Added static grouping for activation heuristic in GPTQ.
- Fixed bug in how quantization scales are computed for Conv2D layer with per-block quantization in GPTQ .
- Can now perform activation only quantization with QAT APIs.
Experimental torch.export conversion support
- Support conversion of stateful models with mutable buffer.
- Support conversion of dynamic inputs shape models.
- Support conversion of 4-bit weight compression models.
Support new torch ops: clip .
Various other bug fixes, enhancements, clean ups and optimizations.
Special thanks to our external contributors for this release: @dpanshu , @timsneath , @kasper0406 , @lamtrinhdev , @valfrom

Appendix

Example code of converting stateful torch.export model ``` import torch import coremltools as ct

class Model(torch.nn.Module): def init(self): super(Model, self).init() self.registerbuffer("state1", torch.tensor([0.0, 0.0, 0.0]))

def forward(self, x):
    # In place update of the model state
    self.state_1.mul_(x)
    return self.state_1 + 1.0

sourcemodel = Model() sourcemodel.eval()

exampleinputs = (torch.tensor([1.0, 2.0, 3.0]),) exportedmodel = torch.export.export(sourcemodel, exampleinputs) coremlmodel = ct.convert(exportedmodel, minimumdeploymenttarget=ct.target.iOS18) * Example code of converting `torch.export` models with dynamic input shapes import torch import coremltools as ct

class Model(torch.nn.Module): def init(self): super(Model, self).init() self.linear = torch.nn.Linear(3, 5)

def forward(self, x):
    y = self.linear(x)
    return y

sourcemodel = Model() sourcemodel.eval()

exampleinputs = (torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),) dynamicshapes = {"x": {0: torch.export.Dim(name="batchdim")}} exportedmodel = torch.export.export(sourcemodel, exampleinputs, dynamicshapes=dynamicshapes) coremlmodel = ct.convert(exportedmodel) * Example code of converting `torch.export` with 4-bit weight compression import torch from torch.export import capturepreautogradgraph from torch.ao.quantization.quantizept2e import convertpt2e, preparept2e from torch.ao.quantization.quantizer.xnnpackquantizer import ( XNNPACKQuantizer, getsymmetricquantization_config, ) import coremltools as ct

class Model(torch.nn.Module): def init(self): super(Model, self).init() self.linear = torch.nn.Linear(3, 5) def forward(self, x): y = self.linear(x) return y

sourcemodel = Model() sourcemodel.eval()

example_inputs = (torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),)

preautogradgraph = capturepreautogradgraph(sourcemodel, exampleinputs) quantizationconfig = getsymmetricquantizationconfig(weightqmin=-7, weightqmax=8) quantizer = XNNPACKQuantizer().setglobal(quantizationconfig) preparedgraph = preparept2e(preautogradgraph, quantizer) convertedgraph = convertpt2e(preparedgraph)

exportedmodel = torch.export.export(convertedgraph, exampleinputs) coremlmodel = ct.convert(exportedmodel, minimumdeployment_target=ct.target.iOS17) ```

- Python
Published by jakesabathia2 almost 2 years ago

coremltools - coremltools 8.0b1

For all the new features, find the updated documentation in the docs-guides

New utilities coremltools.utils.MultiFunctionDescriptor() and coremltools.utils.save_multifunction , for creating an mlprogram with multiple functions in it, that can share weights. Updated the model loading API to load specific functions for prediction.
Stateful Core ML models: updates to the converter to produce Core ML models with the State Type (new type introduced in iOS18/macOS15).
coremltools.optimize
- Updates to model representation (mlprogram) pertaining to compression:
  - Support compression with more granularities: blockwise quantization, grouped channel wise palettization
  - 4 bit weight quantization (in addition to 8 bit quantization that was already supported)
  - 3 bit palettization (in addition to 1,2,4,6,8 bit palettization that was already supported)
  - Support joint compression modes:
    - 8 bit Look-up-tables for palettization
    - ability to combine weight pruning and palettization
    - ability to combine weight pruning and quantization
- API updates:
  - coremltools.optimize.coreml
    - Updated existing APIs to account for features mentioned above
    - Support joint compression by applying compression techniques on an already compressed model
    - A new API to support activation quantization using calibration data, which can be used to take a W16A16 Core ML model and produce a W8A8 model: ct.optimize.coreml.experimental.linear_quantize_activations
      - (to be upgraded from experimental to the official name space in a future release)
  - coremltools.optimize.torch
    - Updated existing APIs to account for features mentioned above
    - Added new APIs for data free compression (PostTrainingPalettizer , PostTrainingQuantizer
    - Added new APIs for calibration data based compression (SKMPalettizer for sensitive k-means palettization algorithm, layerwise_compression for GPTQ/sparseGPT quantization/pruning algorithm)
    - Updated the APIs + the coremltools.convert implementation, so that for converting torch models compressed with ct.optimize.torch , there is no longer a need to provide additional pass pipeline arguments.
iOS18 / macOS15 ops
- compression related ops: constexpr_blockwise_shift_scale, constexpr_lut_to_dense, constexpr_sparse_to_dense, etc
- updates to the GRU op
- PyTorch op scaled_dot_product_attention
Experimental torch.export conversion support

``` import torch import torchvision

import coremltools as ct

torchmodel = torchvision.models.vitb16(weights="IMAGENET1KV1")

x = torch.rand((1, 3, 224, 224)) exampleinputs = (x,) exportedprogram = torch.export.export(torchmodel, exampleinputs)

coremlmodel = ct.convert(exportedprogram) ```

Various other bug fixes, enhancements, clean ups and optimizations

Known Issues

Conversion will fail when using certain palettization modes (e.g. int8 LUT, vector palettization) with torch models using ct.optimize.torch
Some of the joint compression modes when used with the training time APIs in ct.optimize.torch will result in a torch model that is not correctly converted
The post-training palettization config for mlpackage models (ct.optimize.coreml.OpPalettizerConfig) does not yet have all the arguments that are supported in the cto.torch.palettization APIs (e.g. lut_dtype (to get int8 dtyped LUT), cluster_dim (to do vector palettization), enable_per_channel_scale (to apply per-channel-scale) etc).
Applying symmetric quantization using GPTQ algorithm with ct.optimize.torch.layerwise_compression.LayerwiseCompressor will not produce the correct quantization scales, due to a known bug. This may lead to poor accuracy for the quantized model

Special thanks to our external contributors for this release: @teelrabbit @igeni @Cyanosite

- Python
Published by YifanShenSZ almost 2 years ago

coremltools - coremltools 7.2

New Features
- Supports ExecuTorch 0.2 (see ExecuTorch doc for examples)
  - Core ML Partitioner: If a PyTorch model is partially supported with Core ML, then Core ML partitioner can determine the supported part and have ExecuTorch delegate to Core ML.
  - Core ML Quantizer: Quantize PyTorch models in Core ML favored scheme
Enhancements
- Improved Model Conversion Speed
- Expanded Operation Translation Coverage
  - add torch.narrow
  - add torch.adaptive_avg_pool1d and torch.adaptive_max_pool1d
  - add torch.numpy_t (i.e. the numpy-style transpose operator .T)
  - enhance torch.clamp_min for integer data type
  - enhance torch.add for complex data type
  - enhance tf.math.top_k when k is variable

Thanks to our ExecuTorch partners and our open-source community: @KrassCodes @M-Quadra @teelrabbit @minimalic @alealv @ChinChangYang @pcuenca

- Python
Published by YifanShenSZ about 2 years ago

coremltools - coremltools 7.1

New Features:
- Supports Torch 2.1
  - 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
    
    exampleargs = (torch.randn(*size), ) atendialect = export(AnyNNModule(), exampleargs) edgedialect = toedge(atendialect).exportedprogram() edgedialect._dialect = "EDGE"
    
    mlmodel = ct.convert(edge_dialect) ```
Enhancements:
- API - ct.utils.make_pipeline - now allows specifying compute_units
- New optimization passes:
  - Folds selective data movement ops like reshape, transpose into adjacent constant compressed weights
  - Casts int32 → int16 dtype for all intermediate tensors when compute precision is set to fp16
- PyTorch op - multinomial - Adds lowering for it to CoreML
- Type related refinements on Pad and Gather/Gather-like ops
Bug Fixes:
- Fixes coremltools build issue related to kmeans1d package
- Minor fixes in lowering of PyTorch ops: masked_fill & randint
Various other bug fixes, enhancements, clean ups and optimizations.

- Python
Published by DawerG over 2 years ago

coremltools - coremltools 7.0

New submodule 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 here
- coremltools.optimize.torch for compressing torch model with training data and fine-tuning. The fine tuned torch model can then be converted using coremltools.convert
The default neural network backend is now 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.
Python 3.11 support.
Support for new PyTorch ops: 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.
MLModel batch prediction support.
Support for converting statically quantized PyTorch models.
Prediction from compiled model (.modelc files). Get compiled model files from an MLModel instance. Python API to explicitly compile a model.
Faster weight palletization for large tensors.
New utility method for getting weight metadata: coremltools.optimize.coreml.get_weights_metadata. This information can be used to customize optimization across ops when using coremltools.optimize.coreml APIs.
New and updated MIL ops for iOS17/macOS14/watchOS10/tvOS17
coremltools.compression_utils is deprecated.
Changes default I/O type for Neural Networks to FP16 for iOS16/macOS13 or later when mlprogram backend is used.
Changes upper input range behavior when backend is mlprogram:
- If RangeDim is used and no upper-bound is set (with a positive number), an exception will be raised.
- If the user does not use the 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.
Various other bug fixes, enhancements, clean ups and optimizations.

Special thanks to our external contributors for this release: @fukatani , @pcuenca , @KWiecko , @comeweber , @sercand , @mlaves, @cclauss, @smpanaro , @nikalra, @jszaday

- Python
Published by TobyRoseman over 2 years ago

coremltools - coremltools 7.0b2

The default neural network backend is now 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.
Changes default I/O type for Neural Networks to FP16 for iOS16/macOS13 or later when mlprogram backend is used.
Changes upper input range behavior when backend is mlprogram:
- If RangeDim is used and no upper-bound is set (with a positive number), an exception will be raised.
- If the user does not use the 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.
New utility method for getting weight metadata: coremltools.optimize.coreml.get_weights_metadata. This information can be used to customize optimization across ops when using coremltools.optimize.coreml APIs.
Support for new PyTorch ops: repeat_interleave and unflatten.
New and updated iOS17/macOS14 ops: 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.
Various other bug fixes, enhancements, clean ups and optimizations.

Special thanks to our external contributors for this release: @fukatani, @pcuenca, @KWiecko, @comeweber and @sercand

- Python
Published by TobyRoseman almost 3 years ago

coremltools - coremltools 7.0b1

New submodule 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 here
- coremltools.optimize.torch for compressing torch model with training data and fine-tuning. The fine tuned torch model can then be converted using coremltools.convert
Updated MIL ops for iOS17/macOS14/watchOS10/tvOS17
pass_pipeline parameter has been added to coremltools.convert to allow controls over which optimizations are performed.
Python 3.11 support.
MLModel batch prediction support.
Support for converting statically quantized PyTorch models
New Torch layer support: randn, randn_like, scaled_dot_product_attention, stft, tile
Faster weight palletization for large tensors.
coremltools.models.ml_program.compression_utils is deprecated.
Various other bug fixes, enhancements, clean ups and optimizations.

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

- Python
Published by TobyRoseman almost 3 years ago

coremltools - coremltools 6.3

Core ML Tools 6.3 Release Note

Torch 2.0 Support
TensorFlow 2.12.0 Support
Remove Python 3.6 support
Functionality for controling graph passes/optimizations, see the pass_pipeline parameter to coremltools.convert.
A utility function for easily creating pipeline, see: utils.make_pipeline.
A debug utility function for extracting submodels, see: converters.mil.debugging_utils.extract_submodel
Various other bug fixes, enhancements, clean ups and optimizations.

Special thanks to our external contributors for this release: @fukatani, @nikalra and @kevin-keraudren.

- Python
Published by junpeiz about 3 years ago

coremltools - 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.

- Python
Published by junpeiz over 3 years ago

coremltools - 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.

- Python
Published by jakesabathia2 over 3 years ago

coremltools - 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.

- Python
Published by TobyRoseman over 3 years ago

coremltools - 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.

- Python
Published by TobyRoseman almost 4 years ago

coremltools - 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

- Python
Published by TobyRoseman almost 4 years ago