Hi, I'd like to use a MLX model already in the MLX Community in my App. I understand I first need to convert it to Core ML format. Is there an easy way to do that considering MLX is an Apple project? It would be good if it was easier then I'd be more motivated to use MLX to train my models. Thanks Richard

I have a model that uses ‘flatten’, and when I converted it to a Core ML model and ran it on Xcode with an iPhone XR, I noticed that ‘flatten’ was automatically converted to ‘reshape’. However, the NPU does not support ‘reshape’. howerver, I got the Resnet50 model on apple models and performance it on XCode with the same iphone XR, I can see the 'flatten' operator which run on NPU. On the other hand, when I used the following code to convert ResNet50 in PyTorch and ran it on Xcode Performance, the ‘flatten’ operation was converted to ‘reshape’, which then ran on the CPU. ? So I dont know how to keep 'flatten' operator when convert to ml model ? coreml tool 7.1 iphone XR ios 17.5.1 from torchvision import models import coremltools as ct import torch import torch.nn as nn network_name = "my_resnet50" torch_model = models.resnet50(pretrained=True) torch_model.eval() width = 224 height = 224 example_input = torch.rand(1, 3, height, width) traced_model = torch.jit.trace(torch_model, (example_input)) model = ct.convert( traced_model, convert_to = "neuralnetwork", inputs=[ ct.TensorType( name = "data", shape = example_input.shape, dtype = np.float32 ) ], outputs = [ ct.TensorType( name = "output", dtype = np.float32 ) ], compute_units = ct.ComputeUnit.CPU_AND_NE, minimum_deployment_target = ct.target.iOS14, ) model.save("my_resnet.mlmodel") ResNet50 on Resnet50.mlmodel My Convertion of ResNet50

Error when trying to generate CoreML performance report, message says The data couldn't be written because it isn't in the correct format. Here is the code to replicate the issue import numpy as np import coremltools as ct from coremltools.converters.mil import Builder as mb import coremltools.converters.mil as mil w = np.random.normal(size=(256, 128, 1)) wemb = np.random.normal(size=(1, 32000, 128)) # .astype(np.float16) rope_emb = np.random.normal(size=(1, 2048, 128)) shapes = [(1, seqlen) for seqlen in (32, 64)] enum_shape = mil.input_types.EnumeratedShapes(shapes=shapes) fixed_shape = (1, 128) max_length = 2048 dtype = np.float32 @mb.program( input_specs=[ # mb.TensorSpec(enum_shape.symbolic_shape, dtype=mil.input_types.types.int32), mb.TensorSpec(enum_shape.symbolic_shape, dtype=mil.input_types.types.int32), ], opset_version=mil.builder.AvailableTarget.iOS17, ) def flex_like(input_ids): indices = mb.fill_like(ref_tensor=input_ids, value=np.array(1, dtype=np.int32)) causal_mask = np.expand_dims( np.triu(np.full((max_length, max_length), -np.inf, dtype=dtype), 1), axis=0, ) mask = mb.gather( x=causal_mask, indices=indices, axis=2, batch_dims=1, name="mask_gather_0", ) # mask = mb.gather( # x=mask, indices=indices, axis=1, batch_dims=1, name="mask_gather_1" # ) rope = mb.gather(x=rope_emb.astype(dtype), indices=indices, axis=1, batch_dims=1, name="rope") hidden_states = mb.gather(x=wemb.astype(dtype), indices=input_ids, axis=1, batch_dims=1, name="embedding") return ( hidden_states, mask, rope, ) cml_flex_like = ct.convert( flex_like, compute_units=ct.ComputeUnit.ALL, compute_precision=ct.precision.FLOAT32, minimum_deployment_target=ct.target.iOS17, inputs=[ ct.TensorType(name="input_ids", shape=enum_shape), ], ) cml_flex_like.save("flex_like_32") If I remove the hidden states from the return it does work, and it also works if I keep the hidden states, but remove both mask, and rope, i.e, the report is generated for both programs with either these returns: return ( # hidden_states, mask, rope, ) and return ( hidden_states, # mask, # rope, ) It also works if I use a static shape instead of an EnumeratedShape I'm using macOS 15.0 and Xcode 16.0 Edit 1: Forgot to mention that although the performance report fails, the model is still able to make predictions

For some reason YDF does not work with the ARM processor. An issue with mutex and destruction.

I use SoundAnalysis to analyze background sounds and have enabled background permissions. It worked well in previous iOS systems, but a warning appeared in the new iOS18beta version and sound analysis was stopped. Warning List: Execution of the command buffer was aborted due to an error during execution. Insufficient Permission (to submit GPU work from background) [Espresso::handle_ex_plan] exception=Espresso exception: "Generic error": Insufficient Permission (to submit GPU work from background) (00000006:kIOGPUCommandBufferCallbackErrorBackgroundExecutionNotPermitted); code=7 status=-1 Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1). CoreML prediction failed with Error Domain=com.apple.CoreML Code=0 "Failed to evaluate model 0 in pipeline" UserInfo={NSLocalizedDescription=Failed to evaluate model 0 in pipeline, NSUnderlyingError=0x30330e910 {Error Domain=com.apple.CoreML Code=0 "Failed to evaluate model 1 in pipeline" UserInfo={NSLocalizedDescription=Failed to evaluate model 1 in pipeline, NSUnderlyingError=0x303307840 {Error Domain=com.apple.CoreML Code=0 "Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1)." UserInfo={NSLocalizedDescription=Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1).}}}}}

Hi there, I am trying to create a Message Filter app that uses a trained Text Classification to predict scam texts (as it is common in my country and is constantly evolving). However, when I try to use the MLModel in the MessageFilterExtension class, I'm getting initialization of text classifier model with model data failed Here's how I initialize my MLModel that is created using Create ML. do { let model = try MyModel(configuration: .init()) let output = try model.prediction(text: text) guard !output.label.isEmpty else { return nil } return MessagePrediction(rawValue: output.label) } catch { return nil } Is it impossible to use CoreML in Message Filter extensions? Thank you

Hi all, I'm trying to build a scam detection in Message Filter powered by CoreML. I find the predictions of ML reliable and the solution for text frauds and scams are sorely needed. I was able to create a trained MLModel and deploy it in the app. It works on my container app, but when I try to use and initialise the model in the Message Filter extension, I get an error; initialization of text classifier model with model data failed I have tried putting the model in the container app, extension, even made a shared framework for container and extension but to no avail. Every time I invoke the codes to init my model from the extension, I am met with the same error. Here's my code for initializing the model do { let model = try Ace_v24_6(configuration: .init()) let output = try model.prediction(text: text) guard !output.label.isEmpty else { return nil } return MessagePrediction(rawValue: output.label) } catch { return nil } My question is: Is it impossible to use CoreML in MessageFilters? Cheers

I have a couple of models that I want to migrate to .mlpackage but can not find the resources of the session: https://developer.apple.com/videos/play/wwdc2024/10159/ In the video at 21:10 talk about modifications and optimizations, but in the video can not even see the dependencies of the demo. Thanks

Deploy machine learning and AI models on-device with Core ML say the performance report can see the ops run on which unit and why it cannot run on Neural Engine. I tested my model and the report shows a gray checkmark at the Neural Engine, indicating it can run on the Neural Engine. However, it's not executing on the Neural Engine but on the CPU. Why is this happening?

Hi, I want to create a real time sports analytics app that takes camera input and records basketball stats. I want to use pose estimation and object classification to record things such as dribbles, when the ball leaves one's hands. etc. Is it possible to have a model in CoreML that performs pose estimation on people but also does just simple object detection on other classes (ie. ball, hoop?) Thanks

I want to try an any resolution image input Core ML model. So I wrote the model following the Core ML Tools "Set the Range for Each Dimensionas" sample code, modified as below: # Trace the model with random input. example_input = torch.rand(1, 3, 50, 50) traced_model = torch.jit.trace(model.eval(), example_input) # Set the input_shape to use RangeDim for each dimension. input_shape = ct.Shape(shape=(1, 3, ct.RangeDim(lower_bound=25, upper_bound=1920, default=45), ct.RangeDim(lower_bound=25, upper_bound=1920, default=45))) scale = 1/(0.226*255.0) bias = [- 0.485/(0.229) , - 0.456/(0.224), - 0.406/(0.225)] # Convert the model with input_shape. mlmodel = ct.convert(traced_model, inputs=[ct.ImageType(shape=input_shape, name="input", scale=scale, bias=bias)], outputs=[ct.TensorType(name="output")], convert_to="mlprogram", ) # Save the Core ML model mlmodel.save("image_resize_model.mlpackage") It converts OK but when I predict the result with an image It will get the error as below: You will not be able to run predict() on this Core ML model. Underlying exception message was: { NSLocalizedDescription = "Failed to build the model execution plan using a model architecture file '/private/var/folders/8z/vtz02xrj781dxvz1v750skz40000gp/T/model-small.mlmodelc/model.mil' with error code: -7."; } Where did I do wrong?

I was trying the latest coremltools-8.0b1 beta on macOS 15 Beta with the intent to try using the new stateful models api in CoreML. But the conversion would always fail with the error: /AppleInternal/Library/BuildRoots/<snip>/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShadersGraph/mpsgraph/MetalPerformanceShadersGraph/Core/Files/MPSGraphExecutable.mm:162: failed assertion `Error: the minimum deployment target for macOS is 14.0.0' Here's a minimal repro, which works fine with both the stable version of coremltools (7.2) and the beta version (8.0b1) on macOS Sonoma 14.5, but fails with both versions of coremltools on macOS 15.0 Beta and Xcode 16.0 Beta. Which means that this most likely isn't an issue with coremltools, but with the native compilation toolchain. from collections import OrderedDict import coremltools as ct import numpy as np import torch import torch.nn as nn class ResidualAttentionBlock(nn.Module): def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): super().__init__() self.attn = nn.MultiheadAttention(d_model, n_head) self.ln_1 = nn.LayerNorm(d_model) self.mlp = nn.Sequential( OrderedDict( [ ("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", nn.GELU()), ("c_proj", nn.Linear(d_model * 4, d_model)), ] ) ) self.ln_2 = nn.LayerNorm(d_model) self.attn_mask = attn_mask def attention(self, x: torch.Tensor): self.attn_mask = ( self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None ) return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] def forward(self, x: torch.Tensor): x = x + self.attention(self.ln_1(x)) x = x + self.mlp(self.ln_2(x)) return x class Transformer(nn.Module): def __init__( self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None ): super().__init__() self.width = width self.layers = layers self.resblocks = nn.Sequential( *[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)] ) def forward(self, x: torch.Tensor): return self.resblocks(x) transformer = Transformer(width=512, layers=12, heads=8) emb_tokens = torch.rand((1, 512)) ct_model = ct.convert( torch.jit.trace(transformer.eval(), emb_tokens), convert_to="mlprogram", minimum_deployment_target=ct.target.macOS14, inputs=[ct.TensorType(name="embIn", shape=[1, 512])], outputs=[ct.TensorType(name="embOutput", dtype=np.float32)], )

I have several CoreML models that I've set up to run in sequence where one of the outputs from each model is passed as one of the inputs to the next. For the most part, there is very little overhead in between each sub-model "chunk": However a couple of the models (eg the first two above) spend a noticeable amount of time in "Prepare Neural Engine Request". From Instruments, it seems like this is spent doing some sort of model loading. Given that I'm calling these models in sequence and in a fixed order, is there some way to reduce or amortize this cost? Thanks!

"On the latest iOS 18 beta 2, the OCR API，the Translate App and Live Text performs very poorly in recognizing Japanese."

Hi All, I am trying to build a new iOS app by following https://developer.apple.com/videos/play/wwdc2024/10163/?time=67 When I trying to remove all legacy VN I am getting error, I would appreciate if someone can help me get up to speed with the new Vision API

The Keras Embedding layer cannot be calculated on Metal because of the missing Op:StatelessRandomGetKeyCounter, as shown in this error message: tensorflow.python.framework.errors_impl.InvalidArgumentError: Could not satisfy device specification '/job:localhost/replica:0/task:0/device:GPU:0'. enable_soft_placement=0. Supported device types [CPU]. All available devices [/job:localhost/replica:0/task:0/device:GPU:0, /job:localhost/replica:0/task:0/device:CPU:0]. [Op:StatelessRandomGetKeyCounter] A workaround is to enable soft placement, but this obviously is slower: tf.config.set_soft_device_placement(True) Reporting it here as recommended by the TensorFlow Plugin Metal team.

Hi, the following model does not run on ANE. Inspecting with deCoreML I see the error ane: Failed to retrieved zero_point. import numpy as np import coremltools as ct from coremltools.converters.mil import Builder as mb import coremltools.converters.mil as mil B, CIN, COUT = 512, 1024, 1024 * 4 @mb.program( input_specs=[ mb.TensorSpec((B, CIN), mil.input_types.types.fp16), ], opset_version=mil.builder.AvailableTarget.iOS18 ) def prog_manual_dequant( x, ): qw = np.random.randint(0, 2 ** 4, size=(COUT, CIN), dtype=np.int8).astype(mil.mil.types.np_uint4_dtype) scale = np.random.randn(COUT, 1).astype(np.float16) offset = np.random.randn(COUT, 1).astype(np.float16) # offset = np.random.randint(0, 2 ** 4, size=(COUT, 1), dtype=np.uint8).astype(mil.mil.types.np_uint4_dtype) dqw = mb.constexpr_blockwise_shift_scale(data=qw, scale=scale, offset=offset) return mb.linear(x=x, weight=dqw) cml_qmodel = ct.convert( prog_manual_dequant, compute_units=ct.ComputeUnit.CPU_AND_NE, compute_precision=ct.precision.FLOAT16, minimum_deployment_target=ct.target.iOS18, ) Whereas if I use an offset with the same dtype as the weights (uint4 in this case), it does run on ANE Tested on coremltools 8.0b1, on macOS 15.0 beta 2/Xcode 15 beta 2, and macOS 15.0 beta 3/Xcode 15 beta 3.

Hey all 👋🏼 We're currently working on a video processing project using the Vision framework (face, body and hand pose detection), and We've encountered a couple of errors that I need help with. We are on Xcode 16 Beta 3, testing on an iPhone 14 Pro running iOS 18 beta. The error messages are as follows: [LOG_ERROR] /Library/Caches/com.apple.xbs/Sources/MediaAnalysis/VideoProcessing/VCPHumanPoseImageRequest.mm[85]: code 18,446,744,073,709,551,598 encountered an unexpected condition: *** -[__NSArrayM insertObject:atIndex:]: object cannot be nil What we've tried: Debugging: I’ve tried stepping through the code, but the errors occur before I can gather any meaningful insights. Searching Documentation: Looked through Apple’s developer documentation and forums but couldn’t find anything related to these specific error codes. Nil Check: Added checks to ensure objects are not nil before inserting them into arrays, but the error persists. Here are my questions: Has anyone encountered similar errors with the Vision framework, specifically related to VCPHumanPoseImageRequest and NSArray operations? Is there any known issue or bug in the version of the framework I might be using? Could it also be related to the beta? Are there any additional debug steps or logging mechanisms I can implement to narrow down the cause? Any suggestions on how to handle nil objects more effectively in this context? I would greatly appreciate any insights or suggestions you might have. Thank you in advance for your assistance! Thanks all!

I was watching wwdc2024 Deploy machine learning and AI models on-device with Core ML (https://developer.apple.com/videos/play/wwdc2024/10161/) and speaker was showing UI interface where he was ruining on device LLMs / Foundation models. I was wondering if this UI interface is open source and I can download and play around with similar app what was shown:

After I upgraded to MacOS 15 Beta 4(M1 16G), the sampling speed of apple ml-stable-diffusion was about 40% slower than MacOS 14. And when I recompile and run with xcode 16, the following error will appear: loc("EpicPhoto/Unet.mlmodelc/model.mil":2748:12): error: invalid axis: 4294967296, axis must be in range -|rank| <= axis < |rank| Assertion failed: (0 && "failed to infer output types"), function _inferJITOutputTypes, file GPUBaseOps.mm, line 339. I checked the macos 15 release notes and saw that the problem of slow running of Core ML models was fixed, but it didn't seem to be fixed. Fixed: Inference time for large Core ML models is slower than expected on a subset of M-series SOCs (e.g. M1, M1 max) on macOS. (129682801)