chore: import upstream snapshot with attribution

tests/python/target/test_arm_target.py

@@ -0,0 +1,147 @@
+# 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.
+# ruff: noqa: F401
+
+import re
+import subprocess
+import tempfile
+
+import numpy as np
+import pytest
+
+import tvm
+from tvm.script import tirx as T
+from tvm.target import codegen
+from tvm.testing import env
+
+
+@pytest.fixture(scope="session")
+def sve_device_vector_length():
+    c_code = r"""
+    #include <stdio.h>
+    #include <arm_sve.h>
+
+    int main() {
+        printf("%ld\n", svcntb() * 8);
+    }
+    """
+
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        c_path = f"{tmp_dir}/vl.c"
+        o_path = f"{tmp_dir}/out.o"
+        with open(c_path, "w") as f:
+            f.write(c_code)
+        tvm.support.cc.create_executable(o_path, c_path, ["-march=native"])
+        out = subprocess.check_output(o_path, shell=True).strip().decode()
+
+    return int(out)
+
+
+@pytest.mark.skipif(not env.has_cpu_feature("sve"), reason="need aarch64 sve")
+def test_scalable_div(sve_device_vector_length):
+    np.random.seed(0)
+    target = {"kind": "llvm", "mtriple": "aarch64-linux-gnu", "mattr": ["+sve"]}
+    dev = tvm.cpu(0)
+
+    @T.prim_func(s_tir=True)
+    def my_func(a: T.handle):
+        A = T.match_buffer(a, (1,), "int32")
+        T.func_attr({"global_symbol": "my_module", "tirx.noalias": True})
+        A[0] = T.Div(10000, 4 * T.vscale())
+
+    mod = tvm.compile(my_func, target=target)
+
+    A_nd = tvm.runtime.tensor(np.empty((1,), dtype="int32"), device=dev)
+    mod(A_nd)
+
+    ref = 10000 // (sve_device_vector_length // 32)
+    tvm.testing.assert_allclose(A_nd.numpy()[0], ref)
+
+
+@pytest.mark.skipif(not env.has_cpu_feature("sve"), reason="need aarch64 sve")
+def test_scalable_buffer_load_store(sve_device_vector_length):
+    np.random.seed(0)
+    target = {"kind": "llvm", "mtriple": "aarch64-linux-gnu", "mattr": ["+sve"]}
+    num_elements = sve_device_vector_length // 32
+    dev = tvm.cpu(0)
+
+    @T.prim_func(s_tir=True)
+    def my_func(a: T.handle, b: T.handle):
+        A = T.match_buffer(a, (num_elements,), "float32")
+        B = T.match_buffer(b, (num_elements,), "float32")
+        T.func_attr({"global_symbol": "my_module", "tirx.noalias": True})
+        B[T.ramp(0, 1, 4 * T.vscale())] = A[T.ramp(0, 1, 4 * T.vscale())]
+
+    mod = tvm.compile(my_func, target=target)
+
+    A_np = np.random.uniform(size=(num_elements,)).astype("float32")
+    B_np = np.zeros((num_elements,)).astype("float32")
+    A_nd = tvm.runtime.tensor(A_np, device=dev)
+    B_nd = tvm.runtime.tensor(B_np, device=dev)
+    mod(A_nd, B_nd)
+
+    tvm.testing.assert_allclose(B_nd.numpy(), A_np)
+
+
+@pytest.mark.skipif(not env.has_cpu_feature("sve"), reason="need aarch64 sve")
+def test_scalable_loop_bound(sve_device_vector_length):
+    np.random.seed(0)
+
+    dtype = "float32"
+    num_elements = sve_device_vector_length // 32
+    target = {"kind": "llvm", "mtriple": "aarch64-linux-gnu", "mattr": ["+sve"]}
+    dev = tvm.cpu(0)
+
+    @T.prim_func(s_tir=True)
+    def my_func(a: T.handle, b: T.handle):
+        A = T.match_buffer(a, (num_elements,), "float32")
+        B = T.match_buffer(b, (num_elements,), "float32")
+        T.func_attr({"global_symbol": "my_module", "tirx.noalias": True})
+        for i in T.serial(0, 4 * T.vscale()):
+            B[i] = A[i]
+
+    mod = tvm.compile(my_func, target=target)
+
+    A_np = np.random.uniform(size=(num_elements,)).astype(dtype)
+    B_np = np.zeros((num_elements,)).astype(dtype)
+    A_nd = tvm.runtime.tensor(A_np, device=dev)
+    B_nd = tvm.runtime.tensor(B_np, device=dev)
+    mod(A_nd, B_nd)
+
+    tvm.testing.assert_allclose(B_nd.numpy(), A_np)
+
+
+@pytest.mark.skipif(not env.has_cpu_feature("sve"), reason="need aarch64 sve")
+def test_scalable_broadcast(sve_device_vector_length):
+    target = {"kind": "llvm", "mtriple": "aarch64-linux-gnu", "mattr": ["+sve"]}
+    num_elements = sve_device_vector_length // 32
+    dev = tvm.cpu(0)
+
+    @T.prim_func(s_tir=True)
+    def my_func(a: T.handle):
+        A = T.match_buffer(a, (num_elements,), "float32")
+        T.func_attr({"global_symbol": "my_module", "tirx.noalias": True})
+        A[T.ramp(0, 1, 4 * T.vscale())] = T.broadcast(1, 4 * T.vscale())
+
+    mod = tvm.compile(my_func, target=target)
+
+    A_np = np.zeros((num_elements,)).astype("float32")
+    A_nd = tvm.runtime.tensor(A_np, device=dev)
+    mod(A_nd)
+
+    ref = np.ones((num_elements,))
+    tvm.testing.assert_allclose(A_nd.numpy(), ref)