make behaviour of dtype arguments consistent and compliant to numpy (#379)

All functions that take an optional dtype should

* have a default dtype visible in the generated docs (accomplished via `"dtype"_a = std::optional{float32}`)
* behave identical when `dtype=None` or no dtype is passed

This important when passing kw args down from a numpy function like:

```
def f(x, dtype=None):
  mx.random.uniform(dtype=dtype)
  # ...
```

NumPy functions behave like this.

It also fixes a minor bug in `tri`: #378

Closes #378

python/src/ops.cpp

@@ -1212,14 +1212,22 @@ void init_ops(py::module_& m) {
       )pbdoc");
   m.def(
       "linspace",
-      [](Scalar start, Scalar stop, int num, Dtype dtype, StreamOrDevice s) {
+      [](Scalar start,
+         Scalar stop,
+         int num,
+         std::optional<Dtype> dtype,
+         StreamOrDevice s) {
         return linspace(
-            scalar_to_double(start), scalar_to_double(stop), num, dtype, s);
+            scalar_to_double(start),
+            scalar_to_double(stop),
+            num,
+            dtype.value_or(float32),
+            s);
       },
       "start"_a,
       "stop"_a,
       "num"_a = 50,
-      "dtype"_a = float32,
+      "dtype"_a = std::optional{float32},
       "stream"_a = none,
       R"pbdoc(
       linspace(start, stop, num: Optional[int] = 50, dtype: Optional[Dtype] = float32, stream: Union[None, Stream, Device] = None) -> array
@@ -1356,11 +1364,11 @@ void init_ops(py::module_& m) {
         }
       },
       "shape"_a,
-      "dtype"_a = std::nullopt,
+      "dtype"_a = std::optional{float32},
       py::kw_only(),
       "stream"_a = none,
       R"pbdoc(
-        zeros(shape: Union[int, List[int]], dtype: Optional[Dtype] = None, *, stream: Union[None, Stream, Device] = None) -> array
+        zeros(shape: Union[int, List[int]], dtype: Optional[Dtype] = float32, *, stream: Union[None, Stream, Device] = None) -> array
 
         Construct an array of zeros.
 
@@ -1403,11 +1411,11 @@ void init_ops(py::module_& m) {
         }
       },
       "shape"_a,
-      "dtype"_a = std::nullopt,
+      "dtype"_a = std::optional{float32},
       py::kw_only(),
       "stream"_a = none,
       R"pbdoc(
-        ones(shape: Union[int, List[int]], dtype: Optional[Dtype] = None, *, stream: Union[None, Stream, Device] = None) -> array
+        ones(shape: Union[int, List[int]], dtype: Optional[Dtype] = float32, *, stream: Union[None, Stream, Device] = None) -> array
 
         Construct an array of ones.
 
@@ -1449,11 +1457,11 @@ void init_ops(py::module_& m) {
       "n"_a,
       "m"_a = py::none(),
       "k"_a = 0,
-      "dtype"_a = std::nullopt,
+      "dtype"_a = std::optional{float32},
       py::kw_only(),
       "stream"_a = none,
       R"pbdoc(
-      eye(n: int, m: Optional[int] = None, k: int = 0, dtype: Optional[Dtype] = None, *, stream: Union[None, Stream, Device] = None) -> array
+      eye(n: int, m: Optional[int] = None, k: int = 0, dtype: Optional[Dtype] = float32, *, stream: Union[None, Stream, Device] = None) -> array
 
       Create an identity matrix or a general diagonal matrix.
 
@@ -1473,11 +1481,11 @@ void init_ops(py::module_& m) {
         return identity(n, dtype.value_or(float32), s);
       },
       "n"_a,
-      "dtype"_a = std::nullopt,
+      "dtype"_a = std::optional{float32},
       py::kw_only(),
       "stream"_a = none,
       R"pbdoc(
-      identity(n: int, dtype: Optional[Dtype] = None, *, stream: Union[None, Stream, Device] = None) -> array
+      identity(n: int, dtype: Optional[Dtype] = float32, *, stream: Union[None, Stream, Device] = None) -> array
 
       Create a square identity matrix.
 
@@ -1491,13 +1499,17 @@ void init_ops(py::module_& m) {
       )pbdoc");
   m.def(
       "tri",
-      [](int n, std::optional<int> m, int k, Dtype dtype, StreamOrDevice s) {
-        return tri(n, m.value_or(n), k, float32, s);
+      [](int n,
+         std::optional<int> m,
+         int k,
+         std::optional<Dtype> type,
+         StreamOrDevice s) {
+        return tri(n, m.value_or(n), k, type.value_or(float32), s);
       },
       "n"_a,
       "m"_a = none,
       "k"_a = 0,
-      "dtype"_a = float32,
+      "dtype"_a = std::optional{float32},
       py::kw_only(),
       "stream"_a = none,
       R"pbdoc(

python/src/random.cpp

@@ -61,15 +61,21 @@ void init_random(py::module_& parent_module) {
       [](const ScalarOrArray& low,
          const ScalarOrArray& high,
          const std::vector<int>& shape,
-         Dtype type,
+         std::optional<Dtype> type,
          const std::optional<array>& key,
          StreamOrDevice s) {
-        return uniform(to_array(low), to_array(high), shape, type, key, s);
+        return uniform(
+            to_array(low),
+            to_array(high),
+            shape,
+            type.value_or(float32),
+            key,
+            s);
       },
       "low"_a = 0,
       "high"_a = 1,
       "shape"_a = std::vector<int>{},
-      "dtype"_a = float32,
+      "dtype"_a = std::optional{float32},
       "key"_a = none,
       "stream"_a = none,
       R"pbdoc(
@@ -92,12 +98,14 @@ void init_random(py::module_& parent_module) {
   m.def(
       "normal",
       [](const std::vector<int>& shape,
-         Dtype type,
+         std::optional<Dtype> type,
          const std::optional<array>& key,
-         StreamOrDevice s) { return normal(shape, type, key, s); },
+         StreamOrDevice s) {
+        return normal(shape, type.value_or(float32), key, s);
+      },
 
       "shape"_a = std::vector<int>{},
-      "dtype"_a = float32,
+      "dtype"_a = std::optional{float32},
       "key"_a = none,
       "stream"_a = none,
       R"pbdoc(
@@ -116,10 +124,11 @@ void init_random(py::module_& parent_module) {
       [](const ScalarOrArray& low,
          const ScalarOrArray& high,
          const std::vector<int>& shape,
-         Dtype type,
+         std::optional<Dtype> type,
          const std::optional<array>& key,
          StreamOrDevice s) {
-        return randint(to_array(low), to_array(high), shape, type, key, s);
+        return randint(
+            to_array(low), to_array(high), shape, type.value_or(int32), key, s);
       },
       "low"_a,
       "high"_a,
@@ -183,21 +192,22 @@ void init_random(py::module_& parent_module) {
       [](const ScalarOrArray& lower_,
          const ScalarOrArray& upper_,
          const std::optional<std::vector<int>> shape_,
-         Dtype dtype,
+         std::optional<Dtype> type,
          const std::optional<array>& key,
          StreamOrDevice s) {
         auto lower = to_array(lower_);
         auto upper = to_array(upper_);
+        auto t = type.value_or(float32);
         if (shape_.has_value()) {
-          return truncated_normal(lower, upper, shape_.value(), dtype, key, s);
+          return truncated_normal(lower, upper, shape_.value(), t, key, s);
         } else {
-          return truncated_normal(lower, upper, dtype, key, s);
+          return truncated_normal(lower, upper, t, key, s);
         }
       },
       "lower"_a,
       "upper"_a,
       "shape"_a = none,
-      "dtype"_a = float32,
+      "dtype"_a = std::optional{float32},
       "key"_a = none,
       "stream"_a = none,
       R"pbdoc(
@@ -221,9 +231,14 @@ void init_random(py::module_& parent_module) {
       )pbdoc");
   m.def(
       "gumbel",
-      &gumbel,
+      [](const std::vector<int>& shape,
+         std::optional<Dtype> type,
+         const std::optional<array>& key,
+         StreamOrDevice s) {
+        return gumbel(shape, type.value_or(float32), key, s);
+      },
       "shape"_a = std::vector<int>{},
-      "dtype"_a = float32,
+      "dtype"_a = std::optional{float32},
       "stream"_a = none,
       "key"_a = none,
       R"pbdoc(

python/tests/test_ops.py

@@ -325,6 +325,8 @@ class TestOps(mlx_tests.MLXTestCase):
         for shape in [[4], [4, 4], [2, 10]]:
             for diag in [-1, 0, 1, -2]:
                 self.assertCmpNumpy(shape, mx.tri, np.tri, k=diag)
+        self.assertEqual(mx.tri(1, 1).dtype, mx.float32)
+        self.assertEqual(mx.tri(1, 1, dtype=mx.bfloat16).dtype, mx.bfloat16)
 
     def test_tril(self):
         for diag in [-1, 0, 1, -2]:

python/tests/test_random.py

@@ -61,6 +61,8 @@ class TestRandom(mlx_tests.MLXTestCase):
         a = mx.random.uniform(low=-0.1, high=0.1, shape=(1,), dtype=mx.bfloat16)
         self.assertEqual(a.dtype, mx.bfloat16)
 
+        self.assertEqual(mx.random.uniform().dtype, mx.random.uniform(dtype=None).dtype)
+
     def test_normal(self):
         key = mx.random.key(0)
         a = mx.random.normal(key=key)
@@ -78,6 +80,8 @@ class TestRandom(mlx_tests.MLXTestCase):
             a = mx.random.normal(dtype=t)
             self.assertEqual(a.dtype, t)
 
+        self.assertEqual(mx.random.normal().dtype, mx.random.normal(dtype=None).dtype)
+
     def test_randint(self):
         a = mx.random.randint(0, 1, [])
         self.assertEqual(a.shape, [])
@@ -109,6 +113,10 @@ class TestRandom(mlx_tests.MLXTestCase):
         a = mx.random.randint(10, -10, [1000, 1000])
         self.assertTrue(mx.all(a == 10).item())
 
+        self.assertEqual(
+            mx.random.randint(0, 1).dtype, mx.random.randint(0, 1, dtype=None).dtype
+        )
+
     def test_bernoulli(self):
         a = mx.random.bernoulli()
         self.assertEqual(a.shape, [])
@@ -159,6 +167,11 @@ class TestRandom(mlx_tests.MLXTestCase):
         with self.assertRaises(ValueError):
             mx.random.truncated_normal(lower, higher)  # Bad shape
 
+        self.assertEqual(
+            mx.random.truncated_normal(0, 1).dtype,
+            mx.random.truncated_normal(0, 1, dtype=None).dtype,
+        )
+
     def test_gumbel(self):
         samples = mx.random.gumbel(shape=(100, 100))
         self.assertEqual(samples.shape, [100, 100])
@@ -168,6 +181,10 @@ class TestRandom(mlx_tests.MLXTestCase):
         # so this test is pretty conservative
         self.assertTrue(mx.abs(mx.mean(samples) - mean) < 0.2)
 
+        self.assertEqual(
+            mx.random.gumbel((1, 1)).dtype, mx.random.gumbel((1, 1), dtype=None).dtype
+        )
+
     def test_categorical(self):
         logits = mx.zeros((10, 20))
         self.assertEqual(mx.random.categorical(logits, -1).shape, [10])