chore: import upstream snapshot with attribution

2026-07-13 13:36:25 +08:00
commit 26446540fa
3151 changed files with 974126 additions and 0 deletions
@@ -0,0 +1,80 @@
+# 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 functools import partial
+
+import numpy as np
+import pytest
+
+import tvm
+import tvm.testing
+from tvm.script import ir as I
+from tvm.script import tirx as T
+from tvm.testing import env
+
+
+@pytest.mark.gpu
+@pytest.mark.skipif(not env.has_gpu(), reason="need gpu")
+@pytest.mark.parametrize(
+    "target",
+    [
+        pytest.param("cuda", marks=pytest.mark.gpu),
+        pytest.param("metal", marks=pytest.mark.gpu),
+        pytest.param({"kind": "vulkan", "supports_int64": True}, marks=pytest.mark.gpu),
+        pytest.param("opencl", marks=pytest.mark.gpu),
+    ],
+)
+@pytest.mark.parametrize("dtype", ["int32", "uint32", "int64", "uint64"])
+def test_int_intrin(target, dtype):
+    if not tvm.testing.device_enabled(target):
+        pytest.skip(f"{target} not enabled")
+    test_funcs = [
+        (T.clz, lambda x, dtype: int(dtype[-2:]) - (len(bin(x)) - 2)),
+    ]
+
+    for tvm_intrin, np_func in test_funcs:
+        n = 128
+
+        @I.ir_module(s_tir=True)
+        class Module:
+            @T.prim_func(s_tir=True)
+            def main(
+                A: T.Buffer((n,), dtype),
+                B: T.Buffer((n,), dtype),
+            ):
+                T.func_attr({"tirx.noalias": True})
+                for i0 in T.thread_binding(n, thread="threadIdx.x"):
+                    with T.sblock("B"):
+                        v_i0 = T.axis.spatial(n, i0)
+                        T.reads(A[v_i0])
+                        T.writes(B[v_i0])
+                        B[v_i0] = tvm_intrin(A[v_i0])
+
+        f = tvm.compile(Module, target=target)
+
+        def run_and_check():
+            dev = tvm.device(target["kind"] if isinstance(target, dict) else target)
+            a = tvm.runtime.tensor(np.random.randint(0, 100000, size=n).astype(dtype), dev)
+            b = tvm.runtime.tensor(np.zeros(shape=(n,)).astype(dtype), dev)
+            f(a, b)
+            ref = np.vectorize(partial(np_func, dtype=dtype))(a.numpy())
+            tvm.testing.assert_allclose(b.numpy(), ref)
+
+        tvm.testing.run_with_gpu_lock(run_and_check)
+
+
+if __name__ == "__main__":
+    tvm.testing.main()