Some dtype fixes

keras_core/backend/jax/numpy.py

@@ -113,6 +113,13 @@
 
 
 def arange(start, stop=None, step=1, dtype=None):
+    if dtype is None:
+        if hasattr(start, "dtype"):
+            dtype = start.dtype
+        elif isinstance(start, int):
+            dtype = "int32"
+        else:
+            dtype = config.floatx()
     return jnp.arange(start, stop, step=step, dtype=dtype)
 
 

keras_core/backend/numpy/numpy.py

@@ -1,5 +1,8 @@
 import numpy as np
 
+from keras_core.backend import config
+from keras_core.backend import standardize_dtype
+
 
 def add(x1, x2):
     return np.add(x1, x2)
@@ -77,6 +80,13 @@
 
 
 def arange(start, stop=None, step=None, dtype=None):
+    if dtype is None:
+        if hasattr(start, "dtype"):
+            dtype = start.dtype
+        elif isinstance(start, int):
+            dtype = "int32"
+        else:
+            dtype = config.floatx()
     return np.arange(start, stop, step=step, dtype=dtype)
 
 
@@ -124,6 +134,7 @@
 
 
 def array(x, dtype=None):
+    dtype = dtype or config.floatx()
     return np.array(x, dtype=dtype)
 
 
@@ -271,6 +282,7 @@
 
 
 def full(shape, fill_value, dtype=None):
+    dtype = dtype or config.floatx()
     return np.full(shape, fill_value, dtype=dtype)
 
 
@@ -592,7 +604,11 @@
 
 
 def sqrt(x):
-    return np.sqrt(x)
+    dtype = None
+    if hasattr(x, "dtype"):
+        if standardize_dtype(x.dtype).startswith("int"):
+            dtype = config.floatx()
+    return np.sqrt(x, dtype=dtype)
 
 
 def squeeze(x, axis=None):

keras_core/backend/tensorflow/numpy.py

@@ -5,6 +5,7 @@
 import tensorflow as tf
 from tensorflow.experimental import numpy as tfnp
 
+from keras_core.backend import config
 from keras_core.backend.tensorflow.core import convert_to_tensor
 
 
@@ -176,6 +177,13 @@
 def arange(start, stop=None, step=1, dtype=None):
     # tfnp.arange has trouble with dynamic Tensors in compiled function.
     # tf.range does not.
+    if dtype is None:
+        if hasattr(start, "dtype"):
+            dtype = start.dtype
+        elif isinstance(start, int):
+            dtype = "int32"
+        else:
+            dtype = config.floatx()
     return tf.range(start, stop, delta=step, dtype=dtype)
 
 
@@ -749,6 +757,9 @@
 
 
 def sqrt(x):
+    x = convert_to_tensor(x)
+    if tf.as_dtype(x.dtype).is_integer:
+        x = tf.cast(x, dtype=config.floatx())
     return tfnp.sqrt(x)
 
 

keras_core/backend/torch/numpy.py

@@ -1,6 +1,7 @@
 import numpy as np
 import torch
 
+from keras_core.backend import config
 from keras_core.backend.torch.core import cast
 from keras_core.backend.torch.core import convert_to_tensor
 from keras_core.backend.torch.core import get_device
@@ -91,7 +92,7 @@
 
 def zeros_like(x, dtype=None):
     x = convert_to_tensor(x)
-    dtype = to_torch_dtype(dtype)
+    dtype = to_torch_dtype(dtype or x.dtype)
     return torch.zeros_like(x, dtype=dtype)
 
 
@@ -160,6 +161,13 @@
 
 
 def arange(start, stop=None, step=1, dtype=None):
+    if dtype is None:
+        if hasattr(start, "dtype"):
+            dtype = start.dtype
+        elif isinstance(start, int):
+            dtype = "int32"
+        else:
+            dtype = config.floatx()
     dtype = to_torch_dtype(dtype)
     if stop is None:
         return torch.arange(end=start, dtype=dtype, device=get_device())

keras_core/ops/numpy_test.py

@@ -3571,9 +3571,37 @@ def test_split(self):
         self.assertEqual(len(knp.Split(2)(x)), 2)
 
     def test_sqrt(self):
-        x = np.array([[1, 4, 9], [16, 25, 36]])
-        self.assertAllClose(knp.sqrt(x), np.sqrt(x))
-        self.assertAllClose(knp.Sqrt()(x), np.sqrt(x))
+        x = np.array([[1, 4, 9], [16, 25, 36]], dtype="float32")
+        ref_y = np.sqrt(x)
+        y = knp.sqrt(x)
+        self.assertEqual(standardize_dtype(y.dtype), "float32")
+        self.assertAllClose(y, ref_y)
+        y = knp.Sqrt()(x)
+        self.assertEqual(standardize_dtype(y.dtype), "float32")
+        self.assertAllClose(y, ref_y)
+
+    @pytest.mark.skipif(
+        backend.backend() == "jax", reason="JAX does not support float64."
+    )
+    def test_sqrt_float64(self):
+        x = np.array([[1, 4, 9], [16, 25, 36]], dtype="float64")
+        ref_y = np.sqrt(x)
+        y = knp.sqrt(x)
+        self.assertEqual(standardize_dtype(y.dtype), "float64")
+        self.assertAllClose(y, ref_y)
+        y = knp.Sqrt()(x)
+        self.assertEqual(standardize_dtype(y.dtype), "float64")
+        self.assertAllClose(y, ref_y)
+
+    def test_sqrt_int32(self):
+        x = np.array([[1, 4, 9], [16, 25, 36]], dtype="int32")
+        ref_y = np.sqrt(x)
+        y = knp.sqrt(x)
+        self.assertEqual(standardize_dtype(y.dtype), "float32")
+        self.assertAllClose(y, ref_y)
+        y = knp.Sqrt()(x)
+        self.assertEqual(standardize_dtype(y.dtype), "float32")
+        self.assertAllClose(y, ref_y)
 
     def test_stack(self):
         x = np.array([[1, 2, 3], [3, 2, 1]])
@@ -3704,6 +3732,8 @@ def test_arange(self):
         self.assertAllClose(knp.Arange()(3, 7), np.arange(3, 7))
         self.assertAllClose(knp.Arange()(3, 7, 2), np.arange(3, 7, 2))
 
+        self.assertEqual(standardize_dtype(knp.arange(3).dtype), "int32")
+
     def test_full(self):
         self.assertAllClose(knp.full([2, 3], 0), np.full([2, 3], 0))
         self.assertAllClose(knp.full([2, 3], 0.1), np.full([2, 3], 0.1))