chore: import upstream snapshot with attribution

tests/python/relax/test_tvmscript_parser_op_arith_cmp.py

@@ -0,0 +1,189 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+
+from collections.abc import Callable
+
+import pytest
+
+import tvm
+import tvm.script
+import tvm.testing
+from tvm import IRModule, relax
+from tvm.script import relax as R
+
+
+def _check(
+    parsed: relax.Function | IRModule,
+    expect: relax.Function | IRModule | None,
+):
+    test = parsed.script(show_meta=True)
+    roundtrip_mod = tvm.script.from_source(test)
+    tvm.ir.assert_structural_equal(parsed, roundtrip_mod)
+    if expect:
+        tvm.ir.assert_structural_equal(parsed, expect)
+
+
+@pytest.mark.parametrize(
+    "unary_arith_op",
+    [
+        relax.op.abs,
+        relax.op.acos,
+        relax.op.acosh,
+        relax.op.asin,
+        relax.op.asinh,
+        relax.op.atan,
+        relax.op.atanh,
+        relax.op.ceil,
+        relax.op.cos,
+        relax.op.cosh,
+        relax.op.exp,
+        relax.op.floor,
+        relax.op.log,
+        relax.op.negative,
+        relax.op.round,
+        relax.op.rsqrt,
+        relax.op.sigmoid,
+        relax.op.sign,
+        relax.op.sin,
+        relax.op.sinh,
+        relax.op.square,
+        relax.op.sqrt,
+        relax.op.tan,
+        relax.op.tanh,
+    ],
+)
+def test_unary_arith(unary_arith_op: Callable):
+    @R.function
+    def foo(x: R.Tensor((2, 3), "float32")) -> R.Tensor((2, 3), "float32"):
+        gv: R.Tensor((2, 3), "float32") = unary_arith_op(x)
+        return gv
+
+    x = relax.Var("x", R.Tensor((2, 3), "float32"))
+    bb = relax.BlockBuilder()
+    with bb.function("foo", [x]):
+        gv = bb.emit(unary_arith_op(x))
+        bb.emit_func_output(gv)
+
+    _check(foo, bb.get()["foo"])
+
+
+@pytest.mark.parametrize(
+    "unary_check_op",
+    [
+        relax.op.isfinite,
+        relax.op.isinf,
+        relax.op.isnan,
+    ],
+)
+def test_unary_check(unary_check_op: Callable):
+    @R.function
+    def foo(x: R.Tensor((2, 3), "float32")) -> R.Tensor((2, 3), "bool"):
+        gv: R.Tensor((2, 3), "bool") = unary_check_op(x)
+        return gv
+
+    x = relax.Var("x", R.Tensor((2, 3), "float32"))
+    bb = relax.BlockBuilder()
+    with bb.function("foo", [x]):
+        gv = bb.emit(unary_check_op(x))
+        bb.emit_func_output(gv)
+
+    _check(foo, bb.get()["foo"])
+
+
+@pytest.mark.parametrize(
+    "binary_arith_op",
+    [
+        relax.op.add,
+        relax.op.divide,
+        relax.op.floor_divide,
+        relax.op.multiply,
+        relax.op.power,
+        relax.op.subtract,
+        relax.op.maximum,
+        relax.op.minimum,
+    ],
+)
+def test_binary_arith(binary_arith_op: Callable):
+    @R.function
+    def foo(x: R.Tensor((2, 3), "float32"), y: R.Tensor((2, 1), "float32")) -> R.Tensor(
+        (2, 3), "float32"
+    ):
+        gv: R.Tensor((2, 3), "float32") = binary_arith_op(x, y)
+        return gv
+
+    x = relax.Var("x", R.Tensor((2, 3), "float32"))
+    y = relax.Var("y", R.Tensor((2, 1), "float32"))
+    bb = relax.BlockBuilder()
+    with bb.function("foo", [x, y]):
+        gv = bb.emit(binary_arith_op(x, y))
+        bb.emit_func_output(gv)
+
+    _check(foo, bb.get()["foo"])
+
+
+@pytest.mark.parametrize(
+    "binary_cmp_op",
+    [
+        relax.op.equal,
+        relax.op.greater,
+        relax.op.greater_equal,
+        relax.op.less,
+        relax.op.less_equal,
+        relax.op.not_equal,
+    ],
+)
+def test_binary_cmp(binary_cmp_op: Callable):
+    @R.function
+    def foo(x: R.Tensor((2, 3), "float32"), y: R.Tensor((2, 1), "float32")) -> R.Tensor(
+        (2, 3), "bool"
+    ):
+        gv: R.Tensor((2, 3), "bool") = binary_cmp_op(x, y)
+        return gv
+
+    x = relax.Var("x", R.Tensor((2, 3), "float32"))
+    y = relax.Var("y", R.Tensor((2, 1), "float32"))
+    bb = relax.BlockBuilder()
+    with bb.function("foo", [x, y]):
+        gv = bb.emit(binary_cmp_op(x, y))
+        bb.emit_func_output(gv)
+
+    _check(foo, bb.get()["foo"])
+
+
+def test_relax_ewise_fma():
+    @R.function
+    def foo(
+        x: R.Tensor((2, 3, 4), dtype="float32"),
+        y: R.Tensor((2, 3, 4), dtype="float32"),
+        z: R.Tensor((2, 3, 4), dtype="float32"),
+    ) -> R.Tensor((2, 3, 4), dtype="float32"):
+        gv: R.Tensor((2, 3, 4), dtype="float32") = R.ewise_fma(x, y, z)
+        return gv
+
+    x = relax.Var("x", R.Tensor((2, 3, 4), "float32"))
+    y = relax.Var("y", R.Tensor((2, 3, 4), "float32"))
+    z = relax.Var("z", R.Tensor((2, 3, 4), "float32"))
+    bb = relax.BlockBuilder()
+    with bb.function("foo", [x, y, z]):
+        gv = bb.emit(relax.op.ewise_fma(x, y, z))
+        bb.emit_func_output(gv)
+
+    _check(foo, bb.get()["foo"])
+
+
+if __name__ == "__main__":
+    tvm.testing.main()