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 +1,153 @@
+# 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: E501
+
+import tvm.testing
+from tvm.ir import Range
+from tvm.relax import TensorType
+from tvm.relax.distributed import DeviceMesh, DTensorType, Placement
+from tvm.script.parser import ir as I
+from tvm.script.parser import relax as R
+from tvm.script.parser import tirx as T
+
+
+def _assert_print(obj, expected):
+    if not isinstance(obj, str):
+        obj = obj.script(verbose_expr=True)
+    obj = obj.strip()
+    assert obj == expected.strip(), "\n" + obj
+
+
+def test_constant():
+    constant = R.dist.const(
+        1,
+        ty=R.DTensor((), "float32", device_mesh=DeviceMesh((2, 2), Range(0, 4)), placement="R, R"),
+    )
+    assert (
+        constant.__str__()
+        == """R.dist.const(1.0, R.DTensor((), "float32", R.device_mesh((2, 2), R.Range(0, 4)), "R, R"))"""
+    )
+
+
+def test_dtensor_type():
+    tensor_ty1 = TensorType((32, 32), "float32")
+    tensor_ty2 = TensorType((32, 32), None)
+    obj0 = DTensorType(tensor_ty1, DeviceMesh((2, 2), Range(0, 4)), Placement.from_text("S[1], R"))
+    assert (
+        obj0.__str__()
+        == """R.DTensor((32, 32), "float32", R.device_mesh((2, 2), R.Range(0, 4)), "S[1], R")"""
+    )
+
+    obj1 = DTensorType(tensor_ty2, DeviceMesh((2, 2), Range(0, 4)), Placement.from_text("S[1], R"))
+    assert (
+        obj1.__str__()
+        == """R.DTensor((32, 32), device_mesh=R.device_mesh((2, 2), R.Range(0, 4)), placement="S[1], R")"""
+    )
+
+    obj2 = DTensorType(tensor_ty2, DeviceMesh((2, 2), [0, 1, 2, 3]), Placement.from_text("S[1], R"))
+    assert (
+        obj2.__str__()
+        == """R.DTensor((32, 32), device_mesh=R.device_mesh((2, 2), [0, 1, 2, 3]), placement="S[1], R")"""
+    )
+
+
+@I.ir_module(s_tir=True)
+class TestModule:
+    I.module_attrs({"device_num": 10})
+    I.module_global_infos(
+        {
+            "mesh": [
+                R.device_mesh((2, 2), I.Range(0, 4)),  # mesh[0]
+                R.device_mesh((1,), I.Range(4, 5)),  # mesh[1]
+            ]
+        }
+    )
+
+    @T.prim_func(s_tir=True)
+    def tir_func(
+        x: T.Buffer((T.int64(128), T.int64(128)), "float32"),
+        y: T.Buffer((T.int64(128), T.int64(128)), "float32"),
+    ):
+        T.func_attr({"tirx.noalias": True})
+        for i, j in T.grid(T.int64(128), T.int64(128)):
+            with T.sblock():
+                vi, vj = T.axis.remap("SS", [i, j])
+                y[vi, vj] = x[vi, vj] + 1.0
+
+    @R.function
+    def foo(
+        x: R.DTensor((128, 128), "float32", device_mesh="mesh[0]", placement="S[0], R"),
+    ) -> R.DTensor((128, 128), "float32", device_mesh="mesh[0]", placement="S[0], R"):
+        gv0 = R.dist.call_tir(
+            TestModule.tir_func,
+            x,
+            R.DTensor(
+                shape=(128, 128), dtype="float32", device_mesh="mesh[0]", placement="S[0], R"
+            ),
+        )
+        return gv0
+
+
+def test_func():
+    _assert_print(
+        TestModule["foo"],
+        """
+# from tvm.script import relax as R
+
+@R.function
+def foo(x: R.DTensor((128, 128), "float32", R.device_mesh((2, 2), R.Range(0, 4)), "S[0], R")) -> R.DTensor((128, 128), "float32", R.device_mesh((2, 2), R.Range(0, 4)), "S[0], R"):
+    gv0 = R.dist.call_tir(tir_func, (x,), out_ty=R.DTensor((128, 128), "float32", R.device_mesh((2, 2), R.Range(0, 4)), "S[0], R"))
+    return gv0
+            """,
+    )
+
+
+def test_module():
+    _assert_print(
+        TestModule,
+        """
+# from tvm.script import ir as I
+# from tvm.script import tirx as T
+# from tvm.tirx.layout import Axis
+# from tvm.script import relax as R
+
+@I.ir_module
+class Module:
+    I.module_attrs({"device_num": 10})
+    I.module_global_infos({"mesh": [R.device_mesh((2, 2), I.Range(0, 4)), R.device_mesh((1,), I.Range(4, 5))]})
+    @T.prim_func(s_tir=True)
+    def tir_func(x: T.Buffer((T.int64(128), T.int64(128)), "float32"), y: T.Buffer((T.int64(128), T.int64(128)), "float32")):
+        T.func_attr({"tirx.noalias": True})
+        # with T.sblock("root"):
+        for i, j in T.grid(T.int64(128), T.int64(128)):
+            with T.sblock(""):
+                vi, vj = T.axis.remap("SS", [i, j])
+                T.reads(x[vi, vj])
+                T.writes(y[vi, vj])
+                y[vi, vj] = x[vi, vj] + T.float32(1.0)
+
+    @R.function
+    def foo(x: R.DTensor((128, 128), "float32", "mesh[0]", "S[0], R")) -> R.DTensor((128, 128), "float32", "mesh[0]", "S[0], R"):
+        cls = Module
+        gv0 = R.dist.call_tir(cls.tir_func, (x,), out_ty=R.DTensor((128, 128), "float32", "mesh[0]", "S[0], R"))
+        return gv0
+    """,
+    )
+
+
+if __name__ == "__main__":
+    tvm.testing.main()