* einsum initial
* fix comma break
* sum axis was wrong
* small cleanups
* python binding
* changed bindings to resemble numpy
* remove todo comment
* comment changes
* add count of operands/inputs
* fail fast if operands list is empty
* ignore comma if no output
* einsum path matching numpy
* getting somewhere with path
* remove print
* it passes the first test
* moved einsum tests to seperate file
* seperated einsum path
* moved einsum naive
* remove space from equation
* fast fail if no operands passed
* update tests and remove printf
* small cleanup
* some more cleanups
* removed python helper file
* ack
* utilize std for finding min in vector
* duplicate def
* remove the tuple as it was unreadable
* moved einsum_naive back to ops
* remaining isn't needed
* avoid creating another set
* cleanup
* greedy path, start of naive einsum
* more einsum
* fix some bugs
* some more fixes, tests pass
* benchmark
* some simplify
* fix einsum and test
Co-authored-by: Angelos Katharopoulos <a_katharopoulos@apple.com>
* add a bunch more tests and fix a bunch more bugs
* some docs nits
---------
Co-authored-by: dc-dc-dc <dgcruz983@gmail.com>
Co-authored-by: Angelos Katharopoulos <a_katharopoulos@apple.com>
* Added groups to 2-D convolutions. Only implemented for **some** specializations.
Also fixed 1D grouped convs with different kernel strides and added more tests.
* fix channels condition
* add implicit conversion of list to array for equality constraint
* add tests for array equality
* add test for tuple and array equality
* return False if __eq__ arg is list or tuple
* write tests for equality
* update the rule of comparison for __ge__/__gt__/__lt__/__le__
* add a helper function for detecting mlx.core.array
* return true in case fo inequality
* debug minor issue regarding detecting mlx array
* add tests for inequality comparisons
* add name for contribution
* reformat files using pre-commit
* update tests for float
* update tests for inequality
* raise exception in case of invalid comparisons
* use isinstance instead of string comparison
* replace "is_convirtable_to_array" with previous logic
* remove throwing exceptions for other operations
* just a comment
* minor changes for efficiency
* optimize a utils function
* change the function name
* Update ACKNOWLEDGMENTS.md
---------
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
* Fast Inference SDPA op
Implements metal shaders for:
o = mx.fast_inference_sdpa(queries, keys, values, scale, mask)
Supports fp16, fp32 dtypes; assumes d_k = 128.
Generic op support / prompt encoding supported via mlx primitives.
Metal implementation is for the inference use case only.
Majority of performance benefits appears to results from GQA & reduced
bandwidth requirements; there is approximate performance parity for the
MHA use case (from some measurements on M3 Max).
* Flush shared memory to zero before unprotected reads for (scores @ values)
* Move to fast:: namespace, address reviewer comments
... also attempt to revert formatter auto-change for files not relevant
to this change
* Shared memory flush to top of kernel
* Resolve compiler warnings
* Update python/src/fast.cpp
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
* Update python/src/fast.cpp
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
* Update python/src/fast.cpp
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
* Update python/src/fast.cpp
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
* Update docstring per PR feedback
* Softmax in higher precision, ...
* route to fallback for more use cases - batch size > 1, head_dim other
than 128, etc.
* Address linux build failure
* Address other reviewer comments
* Remove extraneous eval_cpu function per review
---------
Co-authored-by: Atila Orhon <64497909+atiorh@users.noreply.github.com>
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
Co-authored-by: atila <atiorh@icloud.com>
* feat: add logicalAnd and logicalOR
* run pre-commit
* Refactor logical_and and logical_or functions
* Add acknowledgement
* Add logical AND and logical OR operators
* Refactor logical_and and logical_or functions
* Add support for logical operators on bool arrays
* Update mlx/ops.cpp
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
* Update mlx/ops.cpp
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
* Add logical AND and OR operators for arrays and scalars
* Refactor vjp and jvp methods in primitives.cpp
* Add overloaded operators for logical AND and OR
* format
---------
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
Co-authored-by: Awni Hannun <awni@apple.com>
* inner / outer impl
* python tests
* ops list and ack
* updated descriptions
* use test helper
* removed dtype check and flatten outer to 1-D
* updated docs
* just use the reshape to flatten
* implemented instancenorm
* implemented vector_norm in cpp
added linalg to mlx
* implemented vector_norm python binding
* renamed vector_norm to norm, implemented norm without provided ord
* completed the implementation of the norm
* added tests
* removed unused import in linalg.cpp
* updated python bindings
* added some tests for python bindings
* handling inf, -inf as numpy does, more extensive tests of compatibility with numpy
* added better docs and examples
* refactored mlx.linalg.norm bindings
* reused existing util for implementation of linalg.norm
* more tests
* fixed a bug with no ord and axis provided
* removed unused imports
* some style and API consistency updates to linalg norm
* remove unused includes
* fix python tests
* fixed a bug with frobenius norm of a complex-valued matrix
* complex for vector too
* addressed PR review comments
* fixed import order in __init__
* expected values in instancenorm tests are simple lists
* minor return expression style change
* added InstanceNorm to docs
* doc string nits
* added myself to individual contributors
---------
Co-authored-by: Awni Hannun <awni@apple.com>
