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 +1,108 @@
+# 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 import relax
+from tvm.script import ir as I
+from tvm.script import relax as R
+from tvm.script import tirx as T
+from tvm.testing import env
+
+add_cuda_source = """
+extern "C" __global__ void add_kernel(float* x, float* y, float* output, int n_elements) {
+    int i = blockIdx.x * blockDim.x + threadIdx.x;
+    if (i < n_elements) {
+        output[i] = x[i] + y[i];
+    }
+}
+"""
+
+
+@pytest.mark.gpu
+@pytest.mark.skipif(not env.has_cuda(), reason="need cuda")
+def test_tir_call_source_kernel():
+    @I.ir_module(s_tir=True)
+    class Module:
+        @T.prim_func(s_tir=True)
+        def add(x_handle: T.handle, y_handle: T.handle, output_handle: T.handle) -> None:
+            T.func_attr({"global_symbol": "add"})
+            m = T.int64()
+            x = T.match_buffer(x_handle, (m,), "float32")
+            y = T.match_buffer(y_handle, (m,), "float32")
+            output = T.match_buffer(output_handle, (m,), "float32")
+            with T.sblock("root"):
+                T.reads(x[0:m], y[0:m])
+                T.writes(output[0:m])
+                BLOCK_SIZE = T.meta_var(64)
+                T.call_kernel(
+                    add_cuda_source,
+                    ((T.ceildiv(m, BLOCK_SIZE),), (BLOCK_SIZE,)),
+                    x.data,
+                    y.data,
+                    output.data,
+                    m,
+                    kernel_name="add_kernel",
+                )
+
+        @R.function
+        def main(x: R.Tensor(("m",), "float32"), y: R.Tensor(("m",), "float32")):
+            m = T.int64()
+            with R.dataflow():
+                output = R.call_tir(Module.add, [x, y], relax.TensorType((m,), "float32"))
+                R.output(output)
+            return output
+
+    @I.ir_module(s_tir=True)
+    class Parsed:
+        @T.prim_func(s_tir=True)
+        def add(x_handle: T.handle, y_handle: T.handle, output_handle: T.handle):
+            m = T.int64()
+            x = T.match_buffer(x_handle, (m,))
+            y = T.match_buffer(y_handle, (m,))
+            output = T.match_buffer(output_handle, (m,))
+            with T.sblock("root"):
+                T.reads(x[0:m], y[0:m])
+                T.writes(output[0:m])
+                T.call_packed(
+                    "add_kernel",
+                    x.data,
+                    y.data,
+                    output.data,
+                    m,
+                    (m + T.int64(64) - T.int64(1)) // T.int64(64),
+                    64,
+                )
+
+    tvm.ir.assert_structural_equal(Module["add"], Parsed["add"])
+    assert len(Module.get_attr("external_mods")) == 1
+
+    with tvm.target.Target("cuda"):
+        lib = tvm.compile(Module)
+
+    def run_and_check():
+        device = tvm.cuda(0)
+        x_nd = tvm.runtime.tensor(np.random.rand(256).astype(np.float32), device)
+        y_nd = tvm.runtime.tensor(np.random.rand(256).astype(np.float32), device)
+        output_np = x_nd.numpy() + y_nd.numpy()
+        output_nd = tvm.runtime.vm.VirtualMachine(lib, device)["main"](x_nd, y_nd)
+        tvm.testing.assert_allclose(output_nd.numpy(), output_np, rtol=1e-5)
+
+    tvm.testing.run_with_gpu_lock(run_and_check)