awni's commit files

2025-10-19 08:38:09 +08:00 · 2023-11-29 10:30:41 -08:00
parent e411fcae68
commit 8ca7f9e8e9
130 changed files with 30159 additions and 0 deletions
--- a/python/tests/test_reduce.py
+++ b/python/tests/test_reduce.py
@@ -0,0 +1,118 @@
+import unittest
+from itertools import permutations, combinations
+
+import mlx.core as mx
+import numpy as np
+
+import mlx_tests
+
+
+class TestReduce(mlx_tests.MLXTestCase):
+    def test_axis_permutation_sums(self):
+        x_npy = np.random.randn(5, 5, 5, 5, 5).astype(np.float32)
+        x_mlx = mx.array(x_npy)
+        for t in permutations(range(5)):
+            with self.subTest(t=t):
+                y_npy = np.transpose(x_npy, t)
+                y_mlx = mx.transpose(x_mlx, t)
+                for n in range(1, 6):
+                    for a in combinations(range(5), n):
+                        with self.subTest(a=a):
+                            z_npy = np.sum(y_npy, axis=a)
+                            z_mlx = mx.sum(y_mlx, axis=a)
+                            mx.eval(z_mlx)
+                            self.assertTrue(
+                                np.allclose(z_npy, np.array(z_mlx), atol=1e-4)
+                            )
+
+    def test_expand_sums(self):
+        x_npy = np.random.randn(5, 1, 5, 1, 5, 1).astype(np.float32)
+        x_mlx = mx.array(x_npy)
+        for m in range(1, 4):
+            for ax in combinations([1, 3, 5], m):
+                shape = np.array([5, 1, 5, 1, 5, 1])
+                shape[list(ax)] = 5
+                shape = shape.tolist()
+                with self.subTest(shape=shape):
+                    y_npy = np.broadcast_to(x_npy, shape)
+                    y_mlx = mx.broadcast_to(x_mlx, shape)
+                    for n in range(1, 7):
+                        for a in combinations(range(6), n):
+                            with self.subTest(a=a):
+                                z_npy = np.sum(y_npy, axis=a) / 1000
+                                z_mlx = mx.sum(y_mlx, axis=a) / 1000
+                                mx.eval(z_mlx)
+                                self.assertTrue(
+                                    np.allclose(z_npy, np.array(z_mlx), atol=1e-4)
+                                )
+
+    def test_dtypes(self):
+        int_dtypes = [
+            "int8",
+            "int16",
+            "int32",
+            "uint8",
+            "uint16",
+            "uint32",
+        ]
+        float_dtypes = ["float32"]
+
+        for dtype in int_dtypes + float_dtypes:
+            with self.subTest(dtype=dtype):
+                x = np.random.uniform(0, 2, size=(3, 3, 3)).astype(getattr(np, dtype))
+                y = mx.array(x)
+
+                for op in ("sum", "prod", "min", "max"):
+                    with self.subTest(op=op):
+
+                        np_op = getattr(np, op)
+                        mlx_op = getattr(mx, op)
+
+                        for axes in (None, 0, 1, 2, (0, 1), (0, 2), (1, 2), (0, 1, 2)):
+                            with self.subTest(axes=axes):
+                                if op in ("sum", "prod"):
+                                    r_np = np_op(
+                                        x, axis=axes, dtype=(getattr(np, dtype))
+                                    )
+                                else:
+                                    r_np = np_op(x, axis=axes)
+                                r_mlx = mlx_op(y, axis=axes)
+                                mx.eval(r_mlx)
+                                self.assertTrue(np.allclose(r_np, r_mlx, atol=1e-4))
+
+    def test_arg_reduce(self):
+        dtypes = [
+            "uint8",
+            "uint16",
+            "uint32",
+            "uint64",
+            "int8",
+            "int16",
+            "int32",
+            "int64",
+            "float16",
+            "float32",
+        ]
+        for dtype in dtypes:
+            with self.subTest(dtype=dtype):
+
+                data = np.random.rand(10, 12, 13).astype(getattr(np, dtype))
+                x = mx.array(data)
+                for op in ["argmin", "argmax"]:
+                    for axis in range(3):
+                        for kd in [True, False]:
+                            a = getattr(mx, op)(x, axis, kd)
+                            b = getattr(np, op)(data, axis, keepdims=kd)
+                            self.assertEqual(a.tolist(), b.tolist())
+
+                for op in ["argmin", "argmax"]:
+                    a = getattr(mx, op)(x, keepdims=True)
+                    b = getattr(np, op)(data, keepdims=True)
+                    self.assertEqual(a.tolist(), b.tolist())
+                    a = getattr(mx, op)(x)
+                    b = getattr(np, op)(data)
+                    self.assertEqual(a.item(), b)
+
+
+if __name__ == "__main__":
+    unittest.main(failfast=True)