chore: import upstream snapshot with attribution

tests/python/codegen/test_inject_ptx_ldg32.py

@@ -0,0 +1,74 @@
+# 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.
+import numpy as np
+import pytest
+
+import tvm
+import tvm.testing
+from tvm.script import tirx as T
+from tvm.testing import env
+
+
+@T.prim_func(s_tir=True)
+def vector_add(A: T.Buffer((16), "float32"), B: T.Buffer((32), "float32")) -> None:
+    T.func_attr({"global_symbol": "default_function", "tirx.noalias": True})
+    bx = T.env_thread("blockIdx.x")
+    tx = T.env_thread("threadIdx.x")
+    T.launch_thread(bx, 1)
+    T.launch_thread(tx, 32)
+    with T.sblock():
+        A_local = T.sblock_alloc_buffer((32), "float32", scope="local")
+
+        with T.sblock():
+            T.reads(A[0:16])
+            T.writes(A_local[0:32])
+            A_local[tx] = T.if_then_else(tx % 2 == 0, A[tx // 2], T.float32(0), dtype="float32")
+            B[tx] = A_local[tx] + 1.0
+
+
+@pytest.mark.gpu
+@pytest.mark.skipif(not env.has_cuda(), reason="need cuda")
+def test_inject_ptx_intrin():
+    f = vector_add
+    arch = tvm.support.nvcc.get_target_compute_version()
+    major, _ = tvm.support.nvcc.parse_compute_version(arch)
+    if major < 8:
+        # Require at least SM80
+        return
+    with tvm.transform.PassContext(config={"tirx.ptx.ldg32": True}):
+        mod = tvm.compile(f, target="cuda")
+    A_np = np.random.rand(16).astype("float32")
+    B_np = np.zeros(32).astype("float32")
+    C_np = np.zeros(32).astype("float32")
+
+    for i in range(32):
+        if i % 2 == 0:
+            C_np[i] = A_np[i // 2]
+        C_np[i] += 1.0
+
+    def run_and_check():
+        dev = tvm.cuda(0)
+        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(), C_np)
+
+    tvm.testing.run_with_gpu_lock(run_and_check)
+
+
+if __name__ == "__main__":
+    test_inject_ptx_intrin()