handle i64 for scatter and cumsum (#19666)

Author: lkarthee

keras/src/backend/mlx/core.py

@@ -71,8 +71,6 @@ def convert_to_tensor(x, dtype=None, sparse=None):
         return x.value
 
     if isinstance(x, np.ndarray):
-        if x.dtype == np.int64:
-            x = x.astype(np.int32)
         x = x.astype(standardize_dtype(x.dtype))
         return mx.array(x, dtype=mlx_dtype)
 
@@ -211,6 +209,10 @@ def vectorized_map(function, elements):
 def scatter(indices, values, shape):
     indices = convert_to_tensor(indices)
     values = convert_to_tensor(values)
+    if values.dtype == mx.int64:
+        values = values.astype(mx.int32)
+    elif values.dtype == mx.uint64:
+        values = values.astype(mx.uint32)
     zeros = mx.zeros(shape, dtype=values.dtype)
     indices = tuple(indices[..., i] for i in range(indices.shape[-1]))
     zeros = zeros.at[indices].add(values)
@@ -222,6 +224,10 @@ def scatter_update(inputs, indices, updates):
     inputs = convert_to_tensor(inputs)
     indices = convert_to_tensor(indices)
     updates = convert_to_tensor(updates)
+    if inputs.dtype == mx.int64:
+        inputs = inputs.astype(mx.int32)
+    elif inputs.dtype == mx.uint64:
+        inputs = inputs.astype(mx.uint32)
     indices = tuple(indices[..., i] for i in range(indices.shape[-1]))
     inputs[indices] = updates
 

keras/src/backend/mlx/numpy.py

@@ -278,13 +278,19 @@ def cumprod(x, axis=None, dtype=None):
     x = convert_to_tensor(x)
     if dtype is not None:
         x = cast(x, dtype)
+    if x.dtype in [mx.int64, mx.uint64]:
+        return mx.cumprod(
+            x, axis=axis, stream=mx.Device(type=mx.DeviceType.cpu)
+        )
     return mx.cumprod(x, axis=axis)
 
 
 def cumsum(x, axis=None, dtype=None):
     x = convert_to_tensor(x)
     if dtype is not None:
         x = cast(x, dtype)
+    if x.dtype in [mx.int64, mx.uint64]:
+        return mx.cumsum(x, axis=axis, stream=mx.Device(type=mx.DeviceType.cpu))
     return mx.cumsum(x, axis=axis)
 
 

keras/src/ops/numpy_test.py

@@ -4943,7 +4943,7 @@ class NumpyDtypeTest(testing.TestCase, parameterized.TestCase):
         ]
     elif backend.backend() == "mlx":
         ALL_DTYPES = [x for x in ALL_DTYPES if x != "float64"]
-        # FLOAT_DTYPES = [x for x in FLOAT_DTYPES if x != "float64" ]
+        FLOAT_DTYPES = tuple([x for x in FLOAT_DTYPES if x != "float64"])
     # Remove float8 dtypes for the following tests
     ALL_DTYPES = [x for x in ALL_DTYPES if x not in dtypes.FLOAT8_TYPES]