Add more MLX dispatches

pytensor/link/mlx/dispatch/__init__.py

@@ -10,4 +10,6 @@
 import pytensor.link.mlx.dispatch.signal
 import pytensor.link.mlx.dispatch.signal.conv
 import pytensor.link.mlx.dispatch.blockwise
+import pytensor.link.mlx.dispatch.extra_ops
+import pytensor.link.mlx.dispatch.sort
 # isort: on

pytensor/link/mlx/dispatch/core.py

@@ -61,14 +61,17 @@ def split(x, axis, splits):
         # Resolve constants for significant performance improvement (14x speedup)
         if constant_axis is not None:
             axis = int(constant_axis)
+        else:
+            raise ValueError(
+                "Symbolic axis is not supported in MLX Split implementation."
+            )
 
         if constant_splits is not None:
-            splits = constant_splits
-            cumsum_splits = np.cumsum(splits[:-1])
+            splits_arr = mx.array(constant_splits)
         else:
-            # Dynamic case - use MLX operations
             splits_arr = mx.array(splits)
-            cumsum_splits = mx.cumsum(splits_arr[:-1]).tolist()
+
+        cumsum_splits = mx.cumsum(splits_arr[:-1]).tolist()
 
         # Validation checks
         if len(splits) != op.len_splits:

pytensor/link/mlx/dispatch/elemwise.py

@@ -1,6 +1,7 @@
 from functools import singledispatch
 
 import mlx.core as mx
+import mlx.nn as mlx_nn
 import numpy as np
 
 from pytensor.link.mlx.dispatch.basic import mlx_funcify
@@ -40,7 +41,7 @@
 )
 from pytensor.scalar.math import Erfc, Erfcx, Sigmoid, Softplus
 from pytensor.tensor.elemwise import CAReduce, DimShuffle, Elemwise
-from pytensor.tensor.special import Softmax, SoftmaxGrad
+from pytensor.tensor.special import LogSoftmax, Softmax, SoftmaxGrad
 
 
 @mlx_funcify.register(DimShuffle)
@@ -142,6 +143,16 @@ def softmax_grad(dy, sm):
     return softmax_grad
 
 
+@mlx_funcify.register(LogSoftmax)
+def mlx_funcify_LogSoftmax(op, **kwargs):
+    axis = op.axis
+
+    def log_softmax(x):
+        return mlx_nn.log_softmax(x, axis=axis)
+
+    return log_softmax
+
+
 @mlx_funcify.register(Softplus)
 def mlx_funcify_Softplus(op, **kwargs):
     def softplus(x):

pytensor/link/mlx/dispatch/extra_ops.py

@@ -0,0 +1,35 @@
+import mlx.core as mx
+
+from pytensor.link.mlx.dispatch.basic import mlx_funcify
+from pytensor.tensor.extra_ops import CumOp, Repeat
+
+
+@mlx_funcify.register(CumOp)
+def mlx_funcify_CumOp(op, **kwargs):
+    axis = op.axis
+    mode = op.mode
+
+    def cumop(x, axis=axis, mode=mode):
+        match mode:
+            case "add":
+                return mx.cumsum(x, axis=axis)
+            case "mul":
+                return mx.cumprod(x, axis=axis)
+            case _:
+                raise NotImplementedError(f"CumOp mode {mode} not implemented in MLX")
+
+    return cumop
+
+
+@mlx_funcify.register(Repeat)
+def jax_funcify_Repeat(op, **kwargs):
+    axis = op.axis
+
+    def repeat(x, repeats, axis=axis):
+        if not isinstance(repeats, int):
+            raise NotImplementedError(
+                "MLX repeat does not support sequence-valued repeat argument."
+            )
+        return mx.repeat(x, repeats, axis=axis)
+
+    return repeat

pytensor/link/mlx/dispatch/sort.py

@@ -0,0 +1,38 @@
+import warnings
+
+import mlx.core as mx
+
+from pytensor.link.mlx.dispatch.basic import mlx_funcify
+from pytensor.tensor.sort import ArgSortOp, SortOp
+
+
+@mlx_funcify.register(SortOp)
+def mlx_funcify_Sort(op, **kwargs):
+    kind = op.kind
+    if kind != "quicksort":
+        warnings.warn(
+            message=f"MLX sort does not support the kind argument (got kind={kind}). The argument will be "
+            f"ignored.",
+            category=UserWarning,
+        )
+
+    def sort(x, axis):
+        return mx.sort(x, axis=axis)
+
+    return sort
+
+
+@mlx_funcify.register(ArgSortOp)
+def mlx_funcify_ArgSort(op, **kwargs):
+    kind = op.kind
+    if kind != "quicksort":
+        warnings.warn(
+            message=f"MLX argsort does not support the kind argument (got kind={kind}). The argument will be "
+            f"ignored.",
+            category=UserWarning,
+        )
+
+    def argsort(x, axis):
+        return mx.argsort(x, axis=axis)
+
+    return argsort

tests/link/mlx/test_core.py

@@ -2,9 +2,15 @@
 import pytest
 
 import pytensor
+from pytensor import config
 from pytensor import tensor as pt
 from pytensor.tensor.basic import Alloc
-from tests.link.mlx.test_basic import compile_mode, mlx_mode_no_compile, mx
+from tests.link.mlx.test_basic import (
+    compare_mlx_and_py,
+    compile_mode,
+    mlx_mode_no_compile,
+    mx,
+)
 
 
 def test_alloc_with_different_shape_types():
@@ -137,3 +143,24 @@ def test_empty_dynamic_shape():
         "used inside compiled functions",
     ):
         f_compiled(3, 4)
+
+
+def test_split_const_axis_const_splits_compiled():
+    x = pt.vector("x")
+    splits = [2, 3]
+    outs = pt.split(x, splits, len(splits), axis=0)
+    compare_mlx_and_py([x], outs, [np.arange(5, dtype="float32")])
+
+
+def test_split_dynamic_axis_const_splits():
+    x = pt.matrix("x")
+    axis = pt.scalar("axis", dtype="int64")
+    splits = [1, 2, 3]
+    outs = pt.split(x, splits, len(splits), axis=axis)
+
+    test_input = np.arange(12).astype(config.floatX).reshape(2, 6)
+
+    with pytest.raises(
+        ValueError, match="Symbolic axis is not supported in MLX Split implementation"
+    ):
+        compare_mlx_and_py([x, axis], outs, [test_input, np.array(1)])

tests/link/mlx/test_elemwise.py

@@ -30,7 +30,7 @@
 from pytensor.tensor.math import max as pt_max
 from pytensor.tensor.math import min as pt_min
 from pytensor.tensor.math import sum as pt_sum
-from pytensor.tensor.special import SoftmaxGrad, softmax
+from pytensor.tensor.special import SoftmaxGrad, log_softmax, softmax
 from pytensor.tensor.type import matrix, vector, vectors
 from tests.link.mlx.test_basic import compare_mlx_and_py
 
@@ -97,6 +97,15 @@ def test_softmax_grad(axis):
     compare_mlx_and_py([dy, sm], [out], [dy_test_value, sm_test_value])
 
 
+@pytest.mark.parametrize("axis", [None, 0, 1])
+def test_logsoftmax(axis):
+    x = matrix("x")
+    x_test_value = np.arange(6, dtype=config.floatX).reshape(2, 3)
+    out = log_softmax(x, axis=axis)
+
+    compare_mlx_and_py([x], [out], [x_test_value])
+
+
 @pytest.mark.parametrize("size", [(10, 10), (1000, 1000)])
 @pytest.mark.parametrize("axis", [0, 1])
 def test_logsumexp_benchmark(size, axis, benchmark):

tests/link/mlx/test_extra_ops.py

@@ -0,0 +1,24 @@
+import numpy as np
+import pytest
+
+from pytensor.configdefaults import config
+from pytensor.tensor import extra_ops as pt_extra_ops
+from pytensor.tensor.type import matrix
+from tests.link.mlx.test_basic import compare_mlx_and_py
+
+
+mx = pytest.importorskip("mlx.core")
+
+
+def test_extra_ops():
+    a = matrix("a")
+    a_test = np.arange(6, dtype=config.floatX).reshape((3, 2))
+
+    out = pt_extra_ops.cumsum(a, axis=0)
+    compare_mlx_and_py([a], [out], [a_test])
+
+    out = pt_extra_ops.cumprod(a, axis=1)
+    compare_mlx_and_py([a], [out], [a_test])
+
+    out = pt_extra_ops.repeat(a, 3, axis=1)
+    compare_mlx_and_py([a], [out], [a_test])

tests/link/mlx/test_sort.py

@@ -0,0 +1,22 @@
+import numpy as np
+import pytest
+
+from pytensor.tensor.sort import argsort, sort
+from pytensor.tensor.type import matrix
+from tests.link.mlx.test_basic import compare_mlx_and_py
+
+
+@pytest.mark.parametrize("axis", [None, -1])
+@pytest.mark.parametrize("func", (sort, argsort))
+def test_sort(func, axis):
+    x = matrix("x", shape=(2, 2), dtype="float64")
+    out = func(x, axis=axis)
+    arr = np.array([[1.0, 4.0], [5.0, 2.0]])
+    compare_mlx_and_py([x], [out], [arr])
+
+
+def test_sort_invalid_kind_warning():
+    x = matrix("x", shape=(2, 2), dtype="float64")
+    z = sort(x, axis=-1, kind="mergesort")
+    with pytest.warns(UserWarning, match="MLX sort does not support the kind argument"):
+        z.eval({x: np.array([[3.0, 1.0], [2.0, 4.0]])}, mode="MLX")