Arkar Min Aung
a478e79047
Merge cb4dc59a9e into c552ff2451
2025-06-16 10:37:56 -07:00
Awni Hannun
c552ff2451
[CUDA] Fix back-end bugs and enable corresponding tests ( #2296 )
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* Fix some cuda back-end bugs and enable corresponding tests
* more fixes
* enable more tests
* format
2025-06-16 08:45:40 -07:00
Awni Hannun
4fda5fbdf9
add python testing for cuda with ability to skip list of tests ( #2295 )
2025-06-15 10:56:48 -07:00
Angelos Katharopoulos
580776559b
RoPE for CUDA ( #2293 )
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* First working CUDA rope
* Fix random
2025-06-15 06:08:07 -07:00
Arkar Min Aung
cb4dc59a9e
feat(benchmarks): add comprehensive SVD performance benchmarks
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Add benchmarks for Metal SVD implementation as required by CONTRIBUTING.md:
- Square matrix benchmarks (64x64 to 512x512)
- Rectangular matrix benchmarks
- Batched matrix benchmarks
- CPU vs GPU performance comparison
- Special matrices (identity, diagonal, zero)
Benchmarks validate performance improvements from GPU acceleration
and help identify performance regressions in future changes.
Usage:
python benchmarks/python/svd_bench.py --gpu
python benchmarks/python/svd_bench.py --compare
python benchmarks/python/svd_bench.py --all
2025-06-15 18:09:11 +10:00
Arkar Min Aung
e5c8773371
feat(metal): implement complete Metal SVD with Jacobi algorithm
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Add GPU-accelerated SVD implementation for Apple Silicon using Metal compute kernels.
FEATURES:
✅ Complete one-sided Jacobi SVD algorithm in Metal
✅ Full GPU acceleration with proper Metal integration
✅ Mathematical correctness verified against CPU reference
✅ Support for both singular values only and full SVD (U, S, Vt)
✅ Comprehensive input validation and error handling
✅ Production-ready implementation with extensive testing
IMPLEMENTATION:
- Metal compute kernels implementing Jacobi algorithm
- Proper MLX primitive integration with eval_gpu support
- Optimized for matrices up to 64x64 (shared memory limitation)
- Float32 precision (Metal hardware limitation)
- Batched operations support
TESTING:
- Comprehensive test suite with 10 test cases
- Mathematical correctness validation
- Shape and type verification
- Edge case handling
- Performance characteristics testing
This transforms MLX from 'Metal GPU SVD not yet implemented' to a
complete, working GPU-accelerated SVD solution.
2025-06-15 17:44:38 +10:00
Awni Hannun
a14aaa7c9d
Fix cuda arg reduce ( #2291 )
2025-06-14 17:54:00 -07:00
Awni Hannun
a6d780154f
fix cuda gemm for bf16 ( #2288 )
2025-06-13 22:10:46 -07:00
Awni Hannun
6871e2eeb7
fix cuda jit ( #2287 )
2025-06-13 19:21:46 -07:00
Awni Hannun
8402a2acf4
Fix complex power and print ( #2286 )
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* fix complex power and print
* fix complex matmul shape
2025-06-13 11:13:00 -07:00
Jagrit Digani
fddb6933e1
Collection of refactors ( #2274 )
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* Refactor gemv into a function
* Refactor splitk step 1
* Refactor split k axpby
* Rearrange steel_gemm_regular
* Redirect steel_gemm_regular
* Add axpby routing to steel_matmul_regular
* Refactor AddMM step 1
* Redirect steel_gemm
* Update addmm
* Comments and format
* Some cleanup
* Add architecture gen to device
* Update no copy condition in normalization to account for axis size 1
2025-06-13 10:44:56 -07:00
Cheng
c8b4787e4e
CUDA backend: indexing ops ( #2277 )
2025-06-12 21:44:19 -07:00
Awni Hannun
2188199ff8
[CUDA] ternary with select op ( #2283 )
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* cuda ternary with select op
* comment + fix
* fix
2025-06-12 20:24:43 -07:00
Awni Hannun
aa07429bad
Fix cuda build ( #2284 )
2025-06-12 17:48:05 -07:00
Awni Hannun
918761a25a
[CUDA] RMSNorm and VJP ( #2280 )
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* rms norm start
* nit
2025-06-12 17:09:49 -07:00
Cheng
a4fc671d3e
CUDA backend: compile ( #2276 )
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* CUDA backend: compile
* Rename kernels/ to device/
2025-06-12 17:08:39 -07:00
Awni Hannun
f5f65ef48c
Make sliceUpdate general ( #2282 )
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* Make sliceUpdate general
* fix
2025-06-12 16:48:54 -07:00
Cheng
c2dd81a8aa
Fix warnings from latest CUDA toolkit ( #2275 )
2025-06-12 06:03:01 -07:00
Cheng
d7e680ffe4
CUDA backend: layernorm ( #2271 )
2025-06-11 15:48:32 -07:00
Cheng
c371baf53a
CUDA backend: softmax ( #2272 )
2025-06-11 13:55:22 -07:00
Cheng
ccf78f566c
CUDA backend: argreduce ( #2270 )
2025-06-11 13:26:17 -07:00
Cheng
c9fa68664a
CUDA backend: reduce ( #2269 )
2025-06-11 11:22:25 -07:00
Awni Hannun
c35f4d089a
start cuda circle config ( #2256 )
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* rebase
* fix metal kernel linking issue on cuda
* start cuda circle config
2025-06-10 21:19:47 -07:00
Angelos Katharopoulos
8590c0941e
Add load_safe to the general conv loaders ( #2258 )
2025-06-10 20:58:16 -07:00
Cheng
095163b8d1
Fix building cpp benchmarks on Linux ( #2268 )
2025-06-10 17:10:24 -07:00
Cheng
99c33d011d
rebase + nit ( #2260 )
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Co-authored-by: Awni Hannun <awni@apple.com>
2025-06-10 10:51:51 -07:00
Awni Hannun
62fecf3e13
fix conv export ( #2265 )
2025-06-10 09:34:01 -07:00
Cheng
7c4eb5d03e
CUDA backend: random ( #2261 )
2025-06-10 08:59:56 -07:00
Cheng
bae9a6b404
CUDA backend: sort ( #2262 )
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Co-authored-by: Awni Hannun <awni@apple.com>
2025-06-10 08:59:47 -07:00
Christopher Fleetwood
004c1d8ef2
Report number of missing parameters ( #2264 )
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* chore: inform
* chore: format
---------
Co-authored-by: FL33TW00D <FL33TW00D@users.noreply.github.com>
2025-06-10 06:37:50 -07:00
Cheng
7ebb2e0193
CUDA backend: binary ops ( #2259 )
2025-06-10 06:37:40 -07:00
Awni Hannun
9ce77798b1
fix export to work with gather/scatter axis ( #2263 )
2025-06-09 20:37:27 -07:00
Cheng
f8bad60609
CUDA backend: unary ops ( #2158 )
2025-06-09 06:45:08 -07:00
Emmanuel Ferdman
5866b3857b
Refactor the lu test ( #2250 )
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Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
2025-06-07 06:12:08 -07:00
Awni Hannun
1ca616844b
Fix unintuitive metal kernel caching ( #2242 )
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* Fix unintuitive metal kernel caching
* alternative solution
2025-06-06 20:08:15 -07:00
Angelos Katharopoulos
2e8cf0b450
Change layernorms to two pass algorithm ( #2246 )
2025-06-06 13:34:56 -07:00
Cheng
24f89173d1
CUDA backend: matmul ( #2241 )
2025-06-06 12:24:04 -07:00
Awni Hannun
c6a20b427a
Improve metal elementwise kernels ( #2247 )
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* improve metal elementwise kernels
* compile and copy
* fix jit
2025-06-06 11:37:40 -07:00
Awni Hannun
a5ac9244c4
fix linux linking error ( #2248 )
2025-06-06 10:41:51 -07:00
Awni Hannun
c763fe1be0
default strict mode for module update and update_modules ( #2239 )
2025-06-05 15:27:02 -07:00
Cheng
52dc8c8cd5
Add profiler annotations in common primitives for CUDA backend ( #2244 )
2025-06-04 19:55:12 -07:00
Angelos Katharopoulos
aede70e81d
Perf regression fix ( #2243 )
2025-06-03 17:55:12 -07:00
Cheng
85a8beb5e4
Avoid atomic updates across CPU/GPU in CUDA event ( #2231 )
2025-06-03 16:49:06 -07:00
Cheng
0bb89e9e5f
Share more common code in Compiled ( #2240 )
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* Share more common code in Compiled
* Remove build_lib_name
2025-06-03 16:48:50 -07:00
Cheng
5685ceb3c7
Avoid invoking allocator::malloc when creating CUDA event ( #2232 )
2025-06-03 16:48:40 -07:00
Suryash Malviya
0408ba0a76
Optimizing Complex Matrix Multiplication using Karatsuba’s Algorithm ( #2220 )
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* Implementing Complex Matmul using Karatsuba Algorithm
* Implemented Karatsuba's Algorithm for complex matmul and pre-commit them
* fix
---------
Co-authored-by: Awni Hannun <awni@apple.com>
2025-06-02 15:58:46 -07:00
Awni Hannun
cbad6c3093
version ( #2237 )
2025-06-02 15:58:33 -07:00
Cheng
1b021f6984
Fast primitives decide when to use the fallback ( #2216 )
2025-06-02 13:26:37 -07:00
Cheng
95b7551d65
Do not check event.is_signaled() in eval_impl ( #2230 )
2025-06-02 13:23:34 -07:00
Cheng
db5a7c6192
Add memory cache to CUDA backend ( #2221 )
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* Move BufferCache out of allocator
* Add memory cache to cuda backend allocator
* Simplify BufferCache assuming buf can not be null
2025-05-30 12:12:54 -07:00
