After watching WWDC 2025 session "Combine Metal 4 machine learning and graphics", I have decided to give it a shot to integrate the latest MTL4MachineLearningCommandEncoder to my existing render pipeline. After a lot of trial and errors, I managed to set up the pipeline and have the app compiled. However, I am now stuck on creating a MTLLibrary with .mtlpackage. Here is the code I have to create a MTLLibrary according the WWDC session https://developer.apple.com/videos/play/wwdc2025/262/?time=550: let coreMLFilePath = bundle.path(forResource: "my_model", ofType: "mtlpackage")! let coreMLURL = URL(string: coreMLFilePath)! do { metalDevice.makeLibrary(URL: coreMLURL) } catch { print("error: \(error)") } With the above code, I am getting error: Error Domain=MTLLibraryErrorDomain Code=1 "Invalid metal package" UserInfo={NSLocalizedDescription=Invalid metal package} What is the correct way to create a MTLLibrary with .mtlpackage? Do I see this error because the .mtlpackage I am using is incorrect? How should I go with debugging this? I'd really appreciate if I could get some help on this as I have been stuck with it for some time now. Thanks in advance!

Hi, I’m testing Unity’s Spaceship HDRP demo on iPhone 17 Pro Max and iPad Pro M4 (iOS 26.1). Everything renders correctly, and my custom MetalFX Spatial plugin initializes successfully — it briefly reports active scaling (e.g. 1434×660 → 2868×1320 at 50% scaling), then reverts to native rendering a few frames later. Setup: Xcode 16.1 (targeting iOS 18) Unity 2022.3.62f3 (HDRP) Metal backend Dynamic Resolution enabled in HDRP assets and cameras Relevant Xcode console excerpt: [MetalFXPlugin] MetalFX_Enable(True) called. [SpaceshipOptions] MetalFX enabled with HDRP dynamic resolution integration. [SpaceshipOptions] Disabled TAA for MetalFX Spatial. [SpaceshipOptions] Created runtime RenderTexture: 1434x660 [MetalFX] Spatial scaler created (1434x660 → 2868x1320). [MetalFX] Processed frame with scaler. [MetalFXPlugin] Sent RenderTexture (1434x660) to MetalFX. Output target 2868x1320. [SpaceshipOptions] MetalFX target set: 1434x660 [SpaceshipOptions] Camera targetTexture cleared after MetalFX handoff. It looks like HDRP clears the camera’s target texture right after MetalFX submits the frame, which causes it to revert to native rendering. Is there a recommended way to persist or rebind the MetalFX output texture when using HDRP on iOS? Unity doesn’t appear to support MetalFX in the Editor either: Thanks!

Hello, I am currently working on a research project under ENINCA Consulting, focused on advanced diagnostic tools for pseudorandom number generators (structural metrics, multi-seed stability, cross-architecture reproducibility, and complementary indicators to TestU01). To validate this diagnostic framework, I prototyped a small non-linear 64-bit PRNG (not as a goal in itself, but simply as a vehicle to test the methodology). During these evaluations, I observed something interesting on Apple Silicon (Mac M1): • bit-exact reproducibility between M1 ARM CPU and M1 Metal GPU, • full BigCrush pass on both CPU and Metal backends, • excellent p-values, • stable behaviour across multiple seeds and runs. This was not the intended objective, the goal was mainly to validate the diagnostic concepts, but these results raised some questions about deterministic compute behaviour in Metal. My question: Is there any official guidance on achieving (or expecting) deterministic RNG or compute behaviour across CPU ↔ Metal GPU on Apple Silicon? More specifically: • Are deterministic compute kernels expected or guaranteed on Metal for scientific workloads? • Are there recommended patterns or best practices to ensure reproducibility across GPU generations (M1 → M2 → M3 → M4)? • Are there known Metal features that can introduce non-determinism? I am not sharing the internal recurrence (this work is proprietary), but I can discuss the high-level diagnostic observations if helpful. Thank you for any insight, very interested in how the Metal engineering team views deterministic compute patterns on Apple Silicon. Pascal ENINCA Consulting

Hello, I am currently working on a research project under ENINCA Consulting, focused on advanced diagnostic tools for pseudorandom number generators (structural metrics, multi-seed stability, cross-architecture reproducibility, and complementary indicators to TestU01). To validate this diagnostic framework, I prototyped a small non-linear 64-bit PRNG (not as a goal in itself, but simply as a vehicle to test the methodology). During these evaluations, I observed something interesting on Apple Silicon (Mac M1): • bit-exact reproducibility between M1 ARM CPU and M1 Metal GPU, • full BigCrush pass on both CPU and Metal backends, • excellent p-values, • stable behaviour across multiple seeds and runs. This was not the intended objective, the goal was mainly to validate the diagnostic concepts, but these results raised some questions about deterministic compute behaviour in Metal. My question: Is there any official guidance on achieving (or expecting) deterministic RNG or compute behaviour across CPU ↔ Metal GPU on Apple Silicon? More specifically: • Are deterministic compute kernels expected or guaranteed on Metal for scientific workloads? • Are there recommended patterns or best practices to ensure reproducibility across GPU generations (M1 → M2 → M3 → M4)? • Are there known Metal features that can introduce non-determinism? I am not sharing the internal recurrence (this work is proprietary), but I can discuss the high-level diagnostic observations if helpful. Thank you for any insight, very interested in how the Metal engineering team views deterministic compute patterns on Apple Silicon. Pascal ENINCA Consulting

Hey I'm using the CIDepthBlurEffect Core Image Filter in my app. It seems to work ok but I get these errors in the console when calling the class. CoreImage Metal library does not contain function for name: sparserendering_xhlrb_scan CoreImage Metal library does not contain function for name: sparserendering_xhlrb_diffuse CoreImage Metal library does not contain function for name: sparserendering_xhlrb_copy_back CoreImage Metal library does not contain function for name: plain_or_sRGB_copy Am I missing some sort of import to gain these Metal functions? I am using my own custom shaders but I assume you'd be able to use them along side the built in ones.

Hi, I would like clarification on whether the new hover effects feature introduced in vision os 26 supported pinch gestures through the psvr 2 controllers. In your sample application, I was not able to confirm that this was working. Only pinch clicking with my hands worked. Pulling the trigger on the controller whilst looking at a 3d object did not activate the hover effect spatial event in the sample application. (The object is showing the highlight though) This is inconsistent with hover effect behavior with psvr2 controllers on swift ui views, where the trigger press does count as a button click. The sample I used was this one: https://developer.apple.com/documentation/compositorservices/rendering_hover_effects_in_metal_immersive_apps

hello apple through this message i want to draw you attention to some problems with gptk and rosetta some games like marvel spiderman 2 have broken animations and t pose issues and other like uncharted and the last of us have severe memory leak issues so its my request please fix it asap

I am trying to create a simple portal like that in RealityKit, but using metal instead of RealityKit. Has anyone been able to create a window or portal like thing to show a skybox outside in mixed Reality?

Hi everyone, I’ve been developing a custom, end-to-end 3D rendering engine called Crescent from scratch using C++20 and Metal-cpp (targeting macOS and visionOS). My primary goal is to build a zero-bottleneck, GPU-driven pipeline that maximizes the potential of Apple Silicon’s Unified Memory and TBDR architecture. While the fundamental systems are stable, I am looking for architectural feedback from Metal framework engineers regarding specific synchronization and latency challenges. Current Core Implementations: GPU-Driven Instance Culling: High-performance occlusion culling using a Hierarchical Z-Buffer (HZB) approach via Compute Shaders. Clustered Forward Shading: Support for high-count dynamic lights through view-space clustering. Temporal Stability: Custom TAA with history rejection and Motion Blur resolve. Asset Infrastructure: Robust GUID-based scene serialization and a JSON-driven ECS hierarchy. The Architectural Challenge: I am currently seeing slight synchronization overhead when generating the HZB mip-chain. On Apple Silicon, I am evaluating the cost of encoder transitions versus cache-friendly barriers. && m_hzbInitPipeline && m_hzbDownsamplePipeline && !m_hzbMipViews.empty(); if (canBuildHzb) { MTL::ComputeCommandEncoder* hzbInit = commandBuffer->computeCommandEncoder(); hzbInit->setComputePipelineState(m_hzbInitPipeline); hzbInit->setTexture(m_depthTexture, 0); hzbInit->setTexture(m_hzbMipViews[0], 1); if (m_pointClampSampler) { hzbInit->setSamplerState(m_pointClampSampler, 0); } else if (m_linearClampSampler) { hzbInit->setSamplerState(m_linearClampSampler, 0); } const uint32_t hzbWidth = m_hzbMipViews[0]->width(); const uint32_t hzbHeight = m_hzbMipViews[0]->height(); const uint32_t threads = 8; MTL::Size tgSize = MTL::Size(threads, threads, 1); MTL::Size gridSize = MTL::Size((hzbWidth + threads - 1) / threads * threads, (hzbHeight + threads - 1) / threads * threads, 1); hzbInit->dispatchThreads(gridSize, tgSize); hzbInit->endEncoding(); for (size_t mip = 1; mip < m_hzbMipViews.size(); ++mip) { MTL::Texture* src = m_hzbMipViews[mip - 1]; MTL::Texture* dst = m_hzbMipViews[mip]; if (!src || !dst) { continue; } MTL::ComputeCommandEncoder* downEncoder = commandBuffer->computeCommandEncoder(); downEncoder->setComputePipelineState(m_hzbDownsamplePipeline); downEncoder->setTexture(src, 0); downEncoder->setTexture(dst, 1); const uint32_t mipWidth = dst->width(); const uint32_t mipHeight = dst->height(); MTL::Size downGrid = MTL::Size((mipWidth + threads - 1) / threads * threads, (mipHeight + threads - 1) / threads * threads, 1); downEncoder->dispatchThreads(downGrid, tgSize); downEncoder->endEncoding(); } if (m_instanceCullHzbPipeline) { dispatchInstanceCulling(m_instanceCullHzbPipeline, true); } } My Questions: Encoder Synchronization: Would you recommend moving this loop into a single ComputeCommandEncoder using MTLBarrier between dispatches to maintain L2 cache residency, or is the overhead of separate encoders negligible for depth-downsampling on TBDR? visionOS Bindless Latency: For stereo rendering on visionOS, what are the best practices for managing MTL4ArgumentTable updates at 90Hz+? I want to ensure that updating bindless resources for each eye doesn't introduce unnecessary CPU-to-GPU latency. Memory Management: Are there specific hints for Memoryless textures that could be applied to intermediate HZB levels to save bandwidth during this process? I’ve attached a screenshot of a scene rendered with the engine (PBR, SSR, and IBL).