This PR implements the numpy.logspace function for the OpenVINO backend as requested in issue #30114.

.gitignore

@@ -19,4 +19,5 @@ examples/**/*.jpg
 .python-version
 .coverage
 *coverage.xml
-.ruff_cache
+.ruff_cache
+venv/

keras/src/backend/openvino/excluded_concrete_tests.txt

@@ -31,7 +31,6 @@ NumpyDtypeTest::test_isinf
 NumpyDtypeTest::test_isnan
 NumpyDtypeTest::test_linspace
 NumpyDtypeTest::test_logaddexp
-NumpyDtypeTest::test_logspace
 NumpyDtypeTest::test_matmul_
 NumpyDtypeTest::test_max
 NumpyDtypeTest::test_mean
@@ -145,7 +144,6 @@ NumpyTwoInputOpsCorrectnessTest::test_divide_no_nan
 NumpyTwoInputOpsCorrectnessTest::test_einsum
 NumpyTwoInputOpsCorrectnessTest::test_inner
 NumpyTwoInputOpsCorrectnessTest::test_linspace
-NumpyTwoInputOpsCorrectnessTest::test_logspace
 NumpyTwoInputOpsCorrectnessTest::test_outer
 NumpyTwoInputOpsCorrectnessTest::test_quantile
 NumpyTwoInputOpsCorrectnessTest::test_take_along_axis

keras/src/backend/openvino/numpy.py

@@ -1029,9 +1029,61 @@ def logical_or(x1, x2):
 
 
 def logspace(start, stop, num=50, endpoint=True, base=10, dtype=None, axis=0):
-    raise NotImplementedError(
-        "`logspace` is not supported with openvino backend"
-    )
+    if dtype is not None:
+        ov_type = OPENVINO_DTYPES[standardize_dtype(dtype)]
+    else:
+        ov_type = OPENVINO_DTYPES[config.floatx()]
+
+    start_t = ov_opset.convert(get_ov_output(start), ov_type).output(0)
+    stop_t = ov_opset.convert(get_ov_output(stop), ov_type).output(0)
+    num_t = ov_opset.convert(get_ov_output(num), ov_type).output(0)
+    base_t = ov_opset.convert(get_ov_output(base), ov_type).output(0)
+
+    if isinstance(num, (int, float)):
+        num_value = int(num)
+    else:
+        num_value = 50
+
+    if endpoint:
+        one = ov_opset.constant(1, ov_type).output(0)
+        divisor = ov_opset.subtract(num_t, one).output(0)
+    else:
+        divisor = num_t
+
+    step_t = ov_opset.divide(
+        ov_opset.subtract(stop_t, start_t).output(0), divisor
+    ).output(0)
+
+    indices_t = ov_opset.range(
+        ov_opset.constant(0, ov_type).output(0),
+        num_t,
+        ov_opset.constant(1, ov_type).output(0),
+        ov_type,
+    ).output(0)
+
+    static_shape = start_t.get_partial_shape().to_shape()
+    if len(static_shape) > 0:
+        reshape_shape = [num_value] + [1] * len(static_shape)
+        indices_t = ov_opset.reshape(
+            indices_t,
+            ov_opset.constant(reshape_shape, dtype=Type.i32).output(0),
+            special_zero=False,
+        ).output(0)
+
+        step_t = ov_opset.reshape(
+            step_t,
+            ov_opset.constant([1] + list(static_shape), dtype=Type.i32).output(
+                0
+            ),
+            special_zero=False,
+        ).output(0)
+
+    linear_t = ov_opset.add(
+        start_t, ov_opset.multiply(indices_t, step_t).output(0)
+    ).output(0)
+    result_t = ov_opset.power(base_t, linear_t).output(0)
+
+    return OpenVINOKerasTensor(result_t)
 
 
 def maximum(x1, x2):

pytest.ini

@@ -0,0 +1,3 @@
+[pytest]
+env =
+    KERAS_BACKEND=openvino