From 1215a85ff3d916634e8bc8a2dd059b9d897dc70f Mon Sep 17 00:00:00 2001
From: Michal Klein <46717574+michalk8@users.noreply.github.com>
Date: Thu, 8 Oct 2026 14:28:23 +0200
Subject: [PATCH] Fix Python dtpye -> mlx conversion narrowing

---
 python/src/convert.cpp     |  8 ++++++--
 python/tests/test_array.py | 12 ++++++++++++
 2 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/python/src/convert.cpp b/python/src/convert.cpp
index eb4e200bac..ac735e1ff3 100644
--- a/python/src/convert.cpp
+++ b/python/src/convert.cpp
@@ -641,6 +641,10 @@ mx::array array_from_list_impl(
         std::vector<uint32_t> vals;
         fill_vector(pl, vals);
         return mx::array(vals.begin(), shape, dtype);
+      } else if (dtype == mx::float64) {
+        std::vector<double> vals;
+        fill_vector(pl, vals);
+        return mx::array(vals.begin(), shape, dtype);
       } else if (mx::issubdtype(dtype, mx::inexact)) {
         std::vector<float> vals;
         fill_vector(pl, vals);
@@ -653,7 +657,7 @@ mx::array array_from_list_impl(
     }
     case pyfloat: {
       auto out_type = specified_type.value_or(mx::float32);
-      if (out_type == mx::float64) {
+      if (out_type == mx::float64 || !mx::issubdtype(out_type, mx::inexact)) {
         std::vector<double> vals;
         fill_vector(pl, vals);
         return mx::array(vals.begin(), shape, out_type);
@@ -748,7 +752,7 @@ mx::array create_array(
     return mx::array(val, t.value_or(default_type));
   } else if (nb::isinstance<nb::float_>(v)) {
     auto out_type = t.value_or(mx::float32);
-    if (out_type == mx::float64) {
+    if (out_type == mx::float64 || !mx::issubdtype(out_type, mx::inexact)) {
       return mx::array(nb::cast<double>(v), out_type);
     } else {
       return mx::array(nb::cast<float>(v), out_type);
diff --git a/python/tests/test_array.py b/python/tests/test_array.py
index bdb80e544a..99c17b8184 100644
--- a/python/tests/test_array.py
+++ b/python/tests/test_array.py
@@ -598,6 +598,18 @@ def test_double_keeps_precision(self):
         out = mx.array([x], dtype=mx.float64).item()
         self.assertEqual(out, x)
 
+        # Integer and bool targets
+        v = 2**24 + 1
+        self.assertEqual(mx.array(float(v), dtype=mx.int32).item(), v)
+        self.assertEqual(mx.array([float(v)], dtype=mx.int64).tolist(), [v])
+        # Small non-zero values are still True
+        self.assertTrue(mx.array(1e-50, dtype=mx.bool_).item())
+        self.assertEqual(mx.array([1e-50], dtype=mx.bool_).tolist(), [True])
+
+        # Python ints to float64
+        out = mx.array([v], dtype=mx.float64).tolist()
+        self.assertEqual(out, [float(v)])
+
     def test_construction_from_lists_wide_ints(self):
         # A python int that does not fit in int32 widens to int64, the same
         # rule the scalar path already uses. It used to raise std::bad_cast.