chore: import upstream snapshot with attribution

tests/python/relax/test_tvmscript_parser_op_linear_algebra.py

@@ -0,0 +1,95 @@
+# 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 tvm
+import tvm.script
+import tvm.testing
+from tvm import IRModule, relax
+from tvm.script import relax as R
+
+
+def _check(
+    parsed: relax.Function | IRModule,
+    expect: relax.Function | IRModule | None,
+):
+    test = parsed.script(show_meta=True)
+    roundtrip_mod = tvm.script.from_source(test)
+    tvm.ir.assert_structural_equal(parsed, roundtrip_mod)
+    if expect:
+        tvm.ir.assert_structural_equal(parsed, expect)
+
+
+def test_matmul():
+    @R.function
+    def foo(
+        x: R.Tensor((2, 3, 4, 5), "float32"), y: R.Tensor((6, 2, 3, 5, 7), "float32")
+    ) -> R.Tensor((6, 2, 3, 4, 7), "float32"):
+        gv: R.Tensor((6, 2, 3, 4, 7), "float32") = R.matmul(x, y)
+        return gv
+
+    x = relax.Var("x", R.Tensor((2, 3, 4, 5), "float32"))
+    y = relax.Var("y", R.Tensor((6, 2, 3, 5, 7), "float32"))
+    bb = relax.BlockBuilder()
+    with bb.function("foo", [x, y]):
+        gv = bb.emit(relax.op.matmul(x, y))
+        bb.emit_func_output(gv)
+
+    _check(foo, bb.get()["foo"])
+
+
+def test_linear():
+    @R.function
+    def foo(
+        x: R.Tensor((2, 3, 4, 5), "float32"),
+        w: R.Tensor((3, 5), "float32"),
+        bias: R.Tensor((3,), "float32"),
+    ):
+        gv = R.linear(x, w, bias)
+        return gv
+
+    x = relax.Var("x", R.Tensor((2, 3, 4, 5), "float32"))
+    w = relax.Var("y", R.Tensor((3, 5), "float32"))
+    bias = relax.Var("bias", R.Tensor((3,), "float32"))
+    bb = relax.BlockBuilder()
+    with bb.function("foo", [x, w, bias]):
+        w_T = bb.emit(relax.op.permute_dims(w, axes=None))
+        matmul = bb.emit(relax.op.matmul(x, w_T))
+        out = matmul + bias
+        bb.emit_func_output(out)
+
+    _check(foo, bb.get()["foo"])
+
+
+def test_einsum():
+    @R.function
+    def foo(x: R.Tensor((1, 4), "float32"), y: R.Tensor((2, 4), "float32")):
+        gv = R.einsum((x, y), "ij, ij -> i")
+        return gv
+
+    x = relax.Var("x", R.Tensor((1, 4), "float32"))
+    y = relax.Var("y", R.Tensor((2, 4), "float32"))
+    bb = relax.BlockBuilder()
+    with bb.function("foo", [x, y]):
+        gv = bb.emit(relax.op.einsum((x, y), "ij, ij -> i"))
+        bb.emit_func_output(gv)
+
+    _check(foo, bb.get()["foo"])
+
+
+if __name__ == "__main__":
+    tvm.testing.main()