Add numpy.linspace to openvino backend

Author: overji

keras/src/backend/openvino/excluded_concrete_tests.txt

@@ -32,7 +32,6 @@ NumpyDtypeTest::test_isclose
 NumpyDtypeTest::test_isfinite
 NumpyDtypeTest::test_isinf
 NumpyDtypeTest::test_isnan
-NumpyDtypeTest::test_linspace
 NumpyDtypeTest::test_log10
 NumpyDtypeTest::test_log1p
 NumpyDtypeTest::test_log
@@ -158,7 +157,6 @@ NumpyTwoInputOpsCorrectnessTest::test_divide_no_nan
 NumpyTwoInputOpsCorrectnessTest::test_einsum
 NumpyTwoInputOpsCorrectnessTest::test_inner
 NumpyTwoInputOpsCorrectnessTest::test_isclose
-NumpyTwoInputOpsCorrectnessTest::test_linspace
 NumpyTwoInputOpsCorrectnessTest::test_logspace
 NumpyTwoInputOpsCorrectnessTest::test_outer
 NumpyTwoInputOpsCorrectnessTest::test_quantile

keras/src/backend/openvino/numpy.py

@@ -823,12 +823,39 @@ def less_equal(x1, x2):
 
 
 def linspace(
-    start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0
+        start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0
 ):
-    raise NotImplementedError(
-        "`linspace` is not supported with openvino backend"
-    )
+    start = get_ov_output(start)
+    stop = get_ov_output(stop)
+
+    if dtype is not None:
+        ov_type = OPENVINO_DTYPES[standardize_dtype(dtype)]
+    else:
+        ov_type = OPENVINO_DTYPES[config.floatx()]
+
+    if num < 0:
+        raise ValueError(f"Number of samples, {num}, must be non-negative.")
+    elif num == 0:
+        range_vals = ov_opset.constant([], dtype=ov_type).output(0)
+        if retstep:
+            return OpenVINOKerasTensor(range_vals), 0
+        return OpenVINOKerasTensor(range_vals)
+
+    start = ov_opset.convert(start, ov_type).output(0)
+    stop = ov_opset.convert(stop, ov_type).output(0)
+
+    if endpoint:
+        num = num - 1
+        step = ov_opset.divide(ov_opset.subtract(stop, start), ov_opset.constant(num, dtype=ov_type)).output(0)
+        stop = ov_opset.add(stop, step).output(0)
+    else:
+        step = ov_opset.divide(ov_opset.subtract(stop, start), ov_opset.constant(num, dtype=ov_type)).output(0)
+
+    range_vals = ov_opset.range(start, stop, step, ov_type).output(0)
 
+    if retstep:
+        return OpenVINOKerasTensor(range_vals), step
+    return OpenVINOKerasTensor(range_vals)
 
 def log(x):
     x = get_ov_output(x)