MLX Backend

[lkarthee]

Issue for tracking and coordinating mlx backend work:

mlx.math

• fft
• fft2
• rfft
• irfft
• stft
• istft
• logsumexp mlx - add missing convert_to_tensor  #19578
• qr
• segment_sum mlx - implement segment_sum and segment_max #19652
• segment_max mlx - implement segment_sum and segment_max #19652
• erfinv feat(math): support erfinv on mlx #19628

mlx.numpy

• einsum
• bincount
• nonzero
• cross
• vdot
• nan_to_num
• copy
• roll
• median Implemented median(...) function. #19568 Implement missing functions in mlx backend #19574
• meshgrid Implement missing functions in mlx backend #19574
• conjugate
• arctan2 [Feature] arctan2 ml-explore/mlx#1065
• quantile
• imag
• real
• select
• argpartition mlx - add argpartition to numpy #19680
• slogdet
• select
• vectorize
• correlate

mlx.image

• rgb_to_grayscale mlx - add rgb_to_grayscale #19609
• resize - mlx - image.resize add crop_to_aspect_ratio argument #19699

mlx.nn

• max_pool
• avg_pool
• conv
• depthwise_conv
• separable_conv
• conv_transpose
• ctc_loss

mlx.rnn

• rnn
• lstm
• gru

mlx.linalg

• cholesky
• det
• eig
• eigh
• inv
• lu_factor
• norm mlx - add linalg.norm #19698
• qr
• solve
• solve_triangular
• svd

mlx.core

• np.ndarray of i64 is being cast to i32 in mlx during conversion if dtype is not passed
• [BUG] mlx crashes with msg - uncaught exception of type std::invalid_argument: [Scatter::eval_gpu] Does not support int64 ml-explore/mlx#1076
• [BUG] np.ndarray of bfloat16 using ml_dtypes is being interpreted as complex64 ml-explore/mlx#1075
• [BUG] arithmetic operations with numpy arrays are not commutative ml-explore/mlx#1066

[lkarthee]

PyTest Output

=========================================================================== test session starts ============================================================================
platform darwin -- Python 3.12.2, pytest-8.1.1, pluggy-1.4.0 -- /Users/kartheek/erlang-ws/github-ws/latest/keras/.venv/bin/python3.12
cachedir: .pytest_cache
rootdir: /Users/kartheek/erlang-ws/github-ws/latest/keras
configfile: pyproject.toml
plugins: cov-5.0.0
collected 6 items

keras/src/ops/operation_test.py::OperationTest::test_autoconfig PASSED                                                                                               [ 16%]
keras/src/ops/operation_test.py::OperationTest::test_eager_call PASSED                                                                                               [ 33%]
keras/src/ops/operation_test.py::OperationTest::test_input_conversion FAILED                                                                                         [ 50%]
keras/src/ops/operation_test.py::OperationTest::test_serialization PASSED                                                                                            [ 66%]
keras/src/ops/operation_test.py::OperationTest::test_symbolic_call PASSED                                                                                            [ 83%]
keras/src/ops/operation_test.py::OperationTest::test_valid_naming PASSED                                                                                             [100%]

================================================================================= FAILURES =================================================================================
___________________________________________________________________ OperationTest.test_input_conversion ____________________________________________________________________

self = <keras.src.ops.operation_test.OperationTest testMethod=test_input_conversion>

    def test_input_conversion(self):
        x = np.ones((2,))
        y = np.ones((2,))
        z = knp.ones((2,))  # mix
        if backend.backend() == "torch":
            z = z.cpu()
        op = OpWithMultipleInputs()
>       out = op(x, y, z)

keras/src/ops/operation_test.py:152:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
keras/src/utils/traceback_utils.py:113: in error_handler
    return fn(*args, **kwargs)
keras/src/ops/operation.py:56: in __call__
    return self.call(*args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <Operation name=op_with_multiple_inputs>, x = array([1., 1.]), y = array([1., 1.])
z = <[ValueError('item can only be called on arrays of size 1.') raised in repr()] array object at 0x13f7450c0>

    def call(self, x, y, z=None):
        # `z` has to be put first due to the order of operations issue with
        # torch backend.
>       return 3 * z + x + 2 * y
E       ValueError: Cannot perform addition on an mlx.core.array and ndarray

keras/src/ops/operation_test.py:14: ValueError
========================================================================= short test summary info ==========================================================================
FAILED keras/src/ops/operation_test.py::OperationTest::test_input_conversion - ValueError: Cannot perform addition on an mlx.core.array and ndarray
======================================================================= 1 failed, 5 passed in 0.13s ========================================================================

How to fix this test case any idea ? add(mx_array, numpy_array) works but fails when using + operator. Should we skip this test for mlx backend ?

[fchollet]

How to fix this test case any idea ? add(mx_array, numpy_array) works but fails when using + operator. Should we skip this test for mlx backend ?

It's not fixable on our side, we should file an issue with the MLX repo. + will hit array.__add__ which is on their side.

[Faisal-Alsrheed]

Thank you for the list.

I am doing

keras/backend/mlx/nn.py:conv
keras/backend/mlx/nn.py:depthwise_conv
keras/backend/mlx/nn.py:separable_conv
keras/backend/mlx/nn.py:conv_transpose

[lkarthee]

I am working on segment_sum, segment_max, max_pool and avg_pool. Thank you .

[yrahul3910]

I want to take a stab at arctan2 (first-time contributor, so I want to start small). I'm working with the mlx team to see if I can add in the required stuff there first, and then I'll add the implementation here.

[lkarthee]

Thank you @yrahul3910 , please go ahead with adding arctan2 impl.

[lkarthee]

mx.matmul and mx.tensordot works only for bfloat16, float16, float32.

FAILED keras/src/ops/numpy_test.py::NumpyDtypeTest::test_tensordot_('int16', 'bool') - ValueError: [matmul] Only real floating point types are supported but int16 and bool were provided which results in int16, which is not a real floating point type.

@fchollet How do we handle this - we can cast integers arguments to float32 if both are integers and result will be float32. If we go this route, we have to modify test cases in numpy_test.py for mlx. Do you have any suggestions.