chore: import upstream snapshot with attribution

tensorflow/compiler/tests/const_test.py

@@ -0,0 +1,81 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for const op compilation."""
+
+import numpy as np
+
+from tensorflow.python.eager import def_function
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import test_util
+from tensorflow.python.platform import test
+
+
+# This test doesn't use XLATestCase like the other tests in this directory.
+# The Const op xla op kernel is compilation only and therefore is not executed
+# with XLA in the on demand compilation mode. Instead we use
+# tf.function(jit_compile=True)
+class ConstOpTest(test_util.TensorFlowTestCase):
+
+  # Verifies that the Const op works
+  # @test_util.run_v2_only
+  def testConst(self):
+    types = {
+        dtypes.bool,
+        dtypes.int8,
+        dtypes.int16,
+        dtypes.int32,
+        dtypes.int64,
+        dtypes.uint8,
+        dtypes.uint16,
+        dtypes.uint32,
+        dtypes.uint64,
+        dtypes.float16,
+        dtypes.bfloat16,
+        dtypes.float32,
+        dtypes.float64,
+        dtypes.float8_e5m2,
+        dtypes.float8_e4m3fn,
+        dtypes.float8_e4m3fnuz,
+        dtypes.float8_e4m3b11fnuz,
+        dtypes.float8_e5m2fnuz,
+    }
+    for dtype in types:
+      with self.subTest(dtype=dtype):
+        if dtype == dtypes.bool:
+          values = [True, False]
+        elif dtype in [
+            dtypes.uint8,
+            dtypes.uint16,
+            dtypes.uint32,
+            dtypes.uint64,
+        ]:
+          values = [0., 1., dtype.min, dtype.max]
+        else:
+          values = [0., 1., -1., dtype.min, dtype.max]
+        if dtype.is_floating:
+          values.extend([float("Inf"), -float("Inf"), float("NaN")])
+        values = np.array(values, dtype=dtype.as_numpy_dtype)
+
+        @def_function.function(jit_compile=True)
+        def f():
+          return constant_op.constant(values, dtype)  # pylint: disable=cell-var-from-loop
+
+        result = f()
+        self.assertAllEqual(self.evaluate(result), values)
+
+
+if __name__ == "__main__":
+  test.main()