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 +1,123 @@
+# 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.
+"""This file tests advanced emit_te features with help of TVMScript assertion"""
+
+# The tests here depend on tvmscript
+
+import tvm
+from tvm import relax as rx
+from tvm import te, tirx
+from tvm.ir.base import assert_structural_equal
+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 test_emit_te_with_symbolic_arg():
+    bb = rx.BlockBuilder()
+    m = tirx.Var("m", "int64")
+    x = rx.Var("x", R.Tensor([10], "float32"))
+    y = rx.Var("y", R.Shape([m]))
+
+    def te_func(A, offset):
+        return te.compute(A.shape, lambda i: A[i + offset], name="B")
+
+    with bb.function("main", [x, y]):
+        out = bb.emit_te(te_func, x, m)
+        bb.emit_func_output(out)
+
+    after = bb.get()
+
+    @I.ir_module(s_tir=True)
+    class Expected:
+        @T.prim_func(private=True, s_tir=True)
+        def te_func(
+            A: T.Buffer((T.int64(10),), "float32"),
+            B: T.Buffer((T.int64(10),), "float32"),
+            m: T.int64,
+        ):
+            T.func_attr({"tirx.noalias": True})
+            for i in range(T.int64(10)):
+                with T.sblock("B"):
+                    v_i = T.axis.spatial(T.int64(10), i)
+                    T.writes(B[v_i])
+                    B[v_i] = A[v_i + m]
+
+        @R.function
+        def main(x: R.Tensor((10,), dtype="float32"), y: R.Shape(["m"])) -> R.Tensor(
+            (10,), dtype="float32"
+        ):
+            m = T.int64()
+            cls = Expected
+            gv = R.call_tir(
+                cls.te_func,
+                (x,),
+                out_ty=R.Tensor((10,), dtype="float32"),
+                tir_vars=R.shape([m]),
+            )
+            return gv
+
+    assert_structural_equal(after, Expected)
+
+
+def test_symbolic_shape_in_prim_value():
+    """Scalar Relax vars may be provided to TE as PrimFunc parameters."""
+
+    def te_slice(tensor, i):
+        return tvm.te.compute([tensor.shape[1]], lambda j: tensor[i, j], name="slice")
+
+    def from_builder():
+        bb = rx.BlockBuilder()
+        A = rx.Var("A", R.Tensor([16, 16], "float32"))
+        relax_i = rx.Var("relax_i", tvm.ir.PrimType("int64"))
+
+        with bb.function("main", params=[A, relax_i]):
+            A_sliced = bb.emit_te(te_slice, A, relax_i)
+            bb.emit_func_output(A_sliced)
+
+        return bb.get()
+
+    @I.ir_module(s_tir=True)
+    class Expected:
+        @T.prim_func(private=True, s_tir=True)
+        def te_slice(
+            A: T.Buffer([T.int64(16), T.int64(16)], "float32"),
+            row_index: T.int64,
+            Output: T.Buffer(T.int64(16), "float32"),
+        ):
+            T.func_attr({"tirx.noalias": True})
+
+            for i in T.serial(T.int64(0), A.shape[1]):
+                with T.sblock("slice"):
+                    vi = T.axis.remap("S", [i])
+                    Output[vi] = A[row_index, vi]
+
+        @R.function
+        def main(
+            A: R.Tensor([16, 16], "float32"),
+            arg_row_index: R.Prim("int64"),
+        ):
+            cls = Expected
+
+            gv = R.call_tir(
+                cls.te_slice,
+                (A, arg_row_index),
+                out_ty=R.Tensor([16], "float32"),
+            )
+            return gv
+
+    tvm.ir.assert_structural_equal(from_builder(), Expected)