metal : initial Metal4 tensor API support #16634
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Any early performance data? |
ggerganov
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@jeffbolznv I think the performance using the tensor API is the same as the old simdgroup-based implementation, but I haven't done detailed analysis yet. I don't have hardware yet to test the actual Neural Accelerators that exist in the new chips and if they would be utilized with these changes. |
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Looking for volunteers with iPhone 17 or MacBook M5 for testing |
I have an iPhone 17, how can I help? |
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I was just curious and don't have an M5. Disregard this if this isn't expected to work outside of that context yet. I see the following diagnostics (MacBook Air M2, Tahoe 26.0.1; Xcode 26.0.1; Apple Clang 17.0.0) when trying to run e.g. llama-server Output |
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@ngladitz Pushed a temporary workaround. It appears that some old versions of the MetalPerformancePrimitives framework are not compatible with Could you provide the output of the following commands: defaults read /System/Library/Frameworks/MetalPerformancePrimitives.framework/Versions/Current/Resources/Info.plist CFBundleShortVersionString
xcrun metal -x metal -E -dM /dev/null |
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@ggerganov Thank you. I ran the download command indicated in that error and reran the initial command: Output |
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Thanks. Does this command produce any output on your end: cat /System/Library/Frameworks/MetalPerformancePrimitives.framework/Versions/Current/Headers/__impl/MPPTensorOpsMatMul2dImpl.h | grep bfloatIn any case, the latest branch should work for you. The expectation is that the performance should be comparable with |
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Yes, thank you the latest branch does build and run with the now presumably expected:
This produces no output. (Quick glance at the file I think I see e.g. |
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Yup, my version has head -n 50 /System/Library/Frameworks/MetalPerformancePrimitives.framework/Versions/A/Headers/MPPTensorOpsMatMul2d.h
// -*- Metal -*-
//===-- MetalTensorOpsMatMul2d
//------------------------------------------------------===//
// Copyright (c) 2025 Apple Inc. All rights reserved
//===----------------------------------------------------------------------===//
// This API performs generalized matrix multiplication operation
// C = A*B + C;
// A and B can be tensor_handle, tensor_offset, and tensor_inline.
// C can be tensor_handle, tensor_offset, tensor_inline or cooperative_tensor.
// Data type combinations supported by this operation are as follows:
//
// A B C
// ---------------------------
// half half half
// half int8_t half
// int8_t half half
// half half float
// half float float
// half int8_t float
// float half float
// float float float
// float int8_t float
// int8_t half float
// int8_t float float
// int8_t int8_t int32_t
// bfloat bfloat bfloat
// bfloat bfloat float
// bfloat float float
// bfloat int8_t bfloat
// bfloat int8_t float
// float bfloat float
// int8_t bfloat bfloat
// int8_t bfloat float
// bfloat half bfloat
// bfloat half half
// bfloat half float
// half bfloat bfloat
// half bfloat half
// half bfloat float
//
// Basic usage is in the following example which takes M x K matrix A of type
// half, K x N matrix B of type half, both in device memory and produces M x N
// matrix C of type float in device memory. It tiles this matrix multiplication
// in thread groups, where each thread group computes a 64 x 32 tile of output
// but multiplying 64 x K tile of A with K x 32 tile of B. This compute kernel
// will be launched with dispatch grid of
//
// MTLSize threadgroups = MTLSizeMake((M + 63)/64, (N + 31)/32, 1);
//So Indeed I am working on the MacOS Developer Beta with Tahoe Beta 26.1, so probably this explains it. |
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Just ran it on M5, this is what I got. It does seem to be working! This branch: Running benchmark...
Loading model... Running benchmark...
Loading model... Running benchmark...
Loading model... Running benchmark...
Main: Running benchmark...
Loading model... Running benchmark...
Loading model... Running benchmark...
Loading model... Running benchmark...
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@mweinbach Is this an M5 iPad? If it is a MacBook, you can simply run |
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Was a Mac, will rerun with llama bench. |
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Here it is with gpt-oss 20b, just to see. About 2x speedup!
build: 9fce244 (6817)
build: 5cca254 (6835) |
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Apple marketing used a 8B llama model, so you can try this as well: llama-bench -m llama-3.1-8b.gguf -p 512,2048 -ub 2048 -fa 1The marketing claimed ~3.5x pp when comparing |
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What about M3 Ultra? |
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Try to share some learnings in github.com/liuliu/example_matmul_metal4 in the past a few days!
Of course, our implementations are vastly different. I am trying to write a new matmul kernel while yours using the existing kernel and leverage the existing dequant to threadgroup memory logic etc. So take whatever I said with a pinch of doubt. All these experiments conducted under iOS / iPad OS 26.0.1 with A19 Pro and M5 (iPad). Also, the tensor ops API is slower than properly implemented GEMM on older devices (MFAv2 GEMM kernels), but that possibly due to wrong tile size selections I do (as I am focusing on neural accelerators performance). |
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The 4x speed up was with Qwen3 8B on this branch of MLX ml-explore/mlx#2687 Not sure what context but at around 20K tokens I saw 3.65x speed up with neural accelerators vs without, and made sure the dtype for the model was fp16, not bf16. Model weights were int4. https://creativestrategies.com/research/m5-apple-silicon-its-all-about-the-cache-and-tensors/ |
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@liuliu Thank you for the insights. AFAICT you don't load the inputs into shared memory and instead use the tensors directly with device memory. Do you think that going through threadgroup memory is redundant when using the Neural Accelerators (i.e. somehow the implementation is clever enough to do it internally for us)? Did you encounter the issue with the missing |
I didn't test it, since 1. we don't have a performant device -> threadgroup loader for MFAv2 (we rely on async_copy_2d previously, but that was removed in macOS 26) to do comparison fairly; 2. In M3 / M4 testing for MFAv2, we found that device -> sram directly seems to be faster than device -> tg memory (with async_copy_2d) -> sram.
Yeah, bfloat is missing for 26.0 as you pointed out, hence following your instruction, I downloaded 26.1 to check it out (WIP!). From what I understand, if you want to test it, you can just JIT a simple shader with the device.makeLibrary function that contains something like (I am pretty sure the code is wrong, but you get the idea). Since it is static assert failure, you can check the failure of compilation for that. Do you know if 26.1 compiled shader works with bfloat on 26.0 or not? That might give indication to see if I can backport bfloat support to 26.0. |
build: 9fce244 (6817)
build: 5cca254 (6835) MBP M5 32GB
( I don't have base M4 to compare outside M4 iPad) |
ggerganov
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Thanks, that worked f2927f4.
I can test this later today - will update my second Mac from Sequoia to Tahoe 26.0 and will build a bfloat shader on 26.1 to test it. @Anemll The pp jump from |
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Posted M5 results for LLAMA2 7B here, so it's apples-to-apples comparison |
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@Anemll Thanks. I think the only thing left for this PR is to confirm that using the Tensor API maintains the performance for old generations (M4 and earlier). I did some early tests on M4 Max and it looked like it did, but need to take a closer look. Btw, could you run one more test for me - I want to see how much the M5 throttles. For example, here is the result on M4 Max: llama-bench -m model.gguf -p 512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512,512ggml_metal_library_init: using embedded metal library
We can see that we immediately lose almost 20% of the performance and later as the test continues this increases up to 30%. I already received a report on my email that M5 does not throttle like this - just want to confirm from a second source. @liuliu I'm a bit late on the backport test - probably will do this over the weekend. Also I am not really sure if my plan would actually work because I suspect that the Tensor API would not be compatible with M2 Ultra (it reports |
It starts fan after a few iterations, with GPU temp rising to 99C, then quickly drops to 93C and remains stable. ./build/bin/llama-bench
build: 9fce244 (6817) anemll@M5 llama.cpp % ./build/bin/llama-bench
build: 9fce244 (6817)
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ggerganov
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26.1 is a release version now, so maybe the best thing to do is to not use the new APIs if on 26.0 entirely... |
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Looking to confirm that the new tensor API does not degrade the performance on pre-M5 chips. If you are on Tahoe 26.1, please run this command and post the results: CMAKE_OPTS="-DGGML_BLAS=OFF" ./scripts/compare-commits.sh 9af8394e5 master test-backend-ops -o MUL_MAT -p "type_a=(bf16|f16|q8_0|q4_0|mxfp4)"M4 Maxggml_metal_device_init: testing tensor API for bfloat support
M4ggml_metal_library_init: using embedded metal library
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This should be ready to merge. I've disable the MPP Tensors on old chips (i.e. pre-M5) because on my M2 Ultra, the current tensor implementation is slightly slower than the original simdgroup-based implementation. Later we can try to close the gap, but for now, I think it's fine to keep the old implementation as is. @liuliu I wasn't able to test "compile on 26.1 -> deploy on 26.0" since my M2 Ultra upgraded directly to 26.1. However, I noticed something unusual - on M2 Ultra, the |
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URL: https://github.com/ArjunDivecha/llama.cpp/tree/metal4-test-harness Metal-4 Tensor Performance Testing ResultsExecutive Summary@ggerganov Tested Metal-4 Tensor implementation on iPhone 17 Pro Max (iOS 26.0.1) Key Result: Metal-4 Tensor delivers 23% performance improvement over Test Configuration
Performance ResultsComparison TableMetal-4 Tensor
Metal Legacy
CPU
Performance Gains
Detailed Analysis1. Throughput PerformanceMetal-4 Tensor achieves 13.66 tokens/second sustained throughput, 2. Time to First Token (TTFT)Metal-4 shows 0.472s TTFT vs Legacy's 0.322s. Slightly higher 3. Thermal ManagementBoth Metal backends maintained "Fair" thermal state throughout 4. Memory UsageMetal backends show similar memory footprint (~4.3GB), with CPU using Technical DetailsImplementationThe Metal-4 Tensor backend uses iOS 26.0.1's native MTLTensor API: case .metalTensor:
model_params.n_gpu_layers = 99
print("Using Metal-4 Tensor backend")
case .metalLegacy:
model_params.n_gpu_layers = 99
print("Using Metal Legacy backend")
case .cpu:
model_params.n_gpu_layers = 0
print("Using CPU backend")
Key Optimizations
- Native MTLTensor API for optimized GPU memory layout
- Kernel fusion reducing memory bandwidth requirements
- Hardware-accelerated matrix multiplication for transformer layers
- Improved thermal management through efficient GPU utilization
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Test Harness
Built comprehensive iOS testing application with:
- Backend Selection: Real-time switching between Metal-4 Tensor, Legacy
Metal, and CPU
- Metrics Collection: TTFT, tokens/sec, memory usage, thermal state
- Automated Comparison: Single-button A/B testing across all backends
- Export: Markdown-formatted results for sharing
Metrics Implementation
public struct InferenceMetrics {
public var backend: Backend
public var ttft: Double
public var tokensPerSecond: Double
public var totalTokens: Int32
public var totalTime: Double
public var memoryUsed: UInt64 // via mach_task_basic_info
public var thermalState: String // via ProcessInfo
}
---
Conclusions
1. Performance Validated: Metal-4 Tensor shows measurable 23% improvement
over Legacy Metal
2. Production Ready: Maintains stable thermal state and memory usage
3. Real-world Impact: 19% faster end-to-end inference on 409-token
generation
4. Scalability: Benefits should increase with larger models and longer
contexts
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Great work on this PR! @ggerganov The Metal-4 Tensor implementation delivers real
performance improvements on iOS 26.0.1. 🎉
As an aside I tested the Mistral 7B IT 7b v0.3 model on Private LLM with the same prompt
and it generated at 5.78 t/s so its likely that its only using the CPU |
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FYI: MLX merged their take on NA: ml-explore/mlx#2772 with OS 26.2 API. OS 26.2 introduced API to set cooperative tensors for input directly, through the |
TODOs
mul_mm_idkernelbfloattensor API? metal : initial Metal4 tensor API support #16634 (comment)