chore: import upstream snapshot with attribution

tests/python/relax/test_transform_bind_symbolic_vars.py

@@ -0,0 +1,268 @@
+# 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 pytest
+
+import tvm
+import tvm.script
+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
+
+
+def test_bind_tensors():
+    """Symbolic variables may occur in Tensor shapes"""
+
+    @tvm.script.ir_module
+    class Before:
+        @R.function
+        def main(
+            x: R.Tensor(("batch", "m"), dtype="float32"),
+            w0: R.Tensor(("m", "n"), dtype="float32"),
+            w1: R.Tensor(("k", 10), dtype="float32"),
+        ) -> R.Tensor(("batch", "k"), dtype="float32"):
+            batch = T.Var("batch", "int64")
+            n = T.Var("n", "int64")
+            k = T.Var("k", "int64")
+            with R.dataflow():
+                lv0 = R.call_dps_packed(
+                    "test0", (x, w0), out_ty=R.Tensor((batch, n), dtype="float32")
+                )
+                out = R.call_dps_packed(
+                    "test1", (lv0, w1), out_ty=R.Tensor((batch, k), dtype="float32")
+                )
+                R.output(out)
+            return out
+
+    symvar_map = {"batch": 1, "k": 3}
+    target_func_name = "main"
+    After = relax.transform.BindSymbolicVars(symvar_map, target_func_name)(Before)
+
+    @I.ir_module
+    class Expected:
+        @R.function
+        def main(
+            x: R.Tensor((1, "m"), dtype="float32"),
+            w0: R.Tensor(("m", "n"), dtype="float32"),
+            w1: R.Tensor((3, 10), dtype="float32"),
+        ) -> R.Tensor((1, 3), dtype="float32"):
+            n = T.int64()
+            with R.dataflow():
+                lv0 = R.call_dps_packed("test0", (x, w0), out_ty=R.Tensor((1, n), dtype="float32"))
+                out = R.call_dps_packed(
+                    "test1", (lv0, w1), out_ty=R.Tensor((1, 3), dtype="float32")
+                )
+                R.output(out)
+            return out
+
+    tvm.ir.assert_structural_equal(Expected, After)
+
+
+def test_bind_shape():
+    """Symbolic variables may occur in ShapeExpr"""
+
+    @tvm.script.ir_module
+    class Before:
+        @R.function
+        def main(
+            x: R.Shape(("batch", "m")),
+            w0: R.Shape(("m", "n")),
+            w1: R.Shape(("k", 10)),
+        ) -> R.Shape(("batch", "k")):
+            batch = T.Var("batch", "int64")
+            n = T.Var("n", "int64")
+            k = T.Var("k", "int64")
+            with R.dataflow():
+                lv0 = R.call_dps_packed("test0", (x, w0), out_ty=R.Tensor((batch, n)))
+                out = R.call_dps_packed("test1", (lv0, w1), out_ty=R.Tensor((batch, k)))
+                R.output(out)
+            return out
+
+    symvar_map = {"batch": 1, "k": 3}
+    target_func_name = "main"
+    After = relax.transform.BindSymbolicVars(symvar_map, target_func_name)(Before)
+
+    @I.ir_module
+    class Expected:
+        @R.function
+        def main(x: R.Shape([1, "m"]), w0: R.Shape(["m", "n"]), w1: R.Shape([3, 10])) -> R.Shape(
+            [1, 3]
+        ):
+            n = T.int64()
+            with R.dataflow():
+                lv0 = R.call_dps_packed("test0", (x, w0), out_ty=R.Tensor((1, n)))
+                out = R.call_dps_packed("test1", (lv0, w1), out_ty=R.Tensor((1, 3)))
+                R.output(out)
+            return out
+
+    tvm.ir.assert_structural_equal(Expected, After)
+
+
+def test_arith():
+    """Symbolic shapes may use TIR arithmetic expressions"""
+
+    @tvm.script.ir_module
+    class Before:
+        @R.function
+        def main(
+            x: R.Tensor(("batch", "m-1"), dtype="float32"),
+            w0: R.Tensor(("m", "n"), dtype="float32"),
+            w1: R.Tensor(("k", 10), dtype="float32"),
+        ) -> R.Tensor(("batch", "k*m"), dtype="float32"):
+            batch = T.Var("batch", "int64")
+            m = T.Var("m", "int64")
+            n = T.Var("n", "int64")
+            k = T.Var("k", "int64")
+            with R.dataflow():
+                lv0 = R.call_dps_packed(
+                    "test0",
+                    (x, w0),
+                    out_ty=R.Tensor((batch, m + n), dtype="float32"),
+                )
+                out = R.call_dps_packed(
+                    "test1",
+                    (lv0, w1),
+                    out_ty=R.Tensor((batch, k + n), dtype="float32"),
+                )
+                R.output(out)
+            return out
+
+    symvar_map = {"batch": 1, "k": 2, "m": 3}
+    target_func_name = "main"
+    After = relax.transform.BindSymbolicVars(symvar_map, target_func_name)(Before)
+
+    @I.ir_module
+    class Expected:
+        @R.function
+        def main(
+            x: R.Tensor((1, 2), dtype="float32"),
+            w0: R.Tensor((3, "n"), dtype="float32"),
+            w1: R.Tensor((2, 10), dtype="float32"),
+        ) -> R.Tensor((1, 6), dtype="float32"):
+            n = T.int64()
+            with R.dataflow():
+                lv0 = R.call_dps_packed(
+                    "test0", (x, w0), out_ty=R.Tensor((1, n + 3), dtype="float32")
+                )
+                out = R.call_dps_packed(
+                    "test1", (lv0, w1), out_ty=R.Tensor((1, n + 2), dtype="float32")
+                )
+                R.output(out)
+            return out
+
+    tvm.ir.assert_structural_equal(Expected, After)
+
+
+def test_bind_multiple_variables_by_name():
+    """String names may be used to replace across multiple functions"""
+
+    @tvm.script.ir_module
+    class Before:
+        @R.function
+        def main_1(x: R.Tensor(("m", "n"), dtype="float32")):
+            return x
+
+        @R.function
+        def main_2(x: R.Tensor(("m", "n"), dtype="float32")):
+            return x
+
+    @tvm.script.ir_module
+    class Expected:
+        @R.function
+        def main_1(x: R.Tensor(("m", 16), dtype="float32")):
+            return x
+
+        @R.function
+        def main_2(x: R.Tensor(("m", 16), dtype="float32")):
+            return x
+
+    After = relax.transform.BindSymbolicVars({"n": 16})(Before)
+    tvm.ir.assert_structural_equal(Expected, After)
+
+
+def test_bind_single_variable_by_identity():
+    """TIR variables may be used to replace a specific var"""
+
+    @tvm.script.ir_module
+    class Before:
+        @R.function
+        def main_1(x: R.Tensor(("m", "n"), dtype="float32")):
+            return x
+
+        @R.function
+        def main_2(x: R.Tensor(("m", "n"), dtype="float32")):
+            return x
+
+    @tvm.script.ir_module
+    class Expected:
+        @R.function
+        def main_1(x: R.Tensor(("m", 16), dtype="float32")):
+            return x
+
+        @R.function
+        def main_2(x: R.Tensor(("m", "n"), dtype="float32")):
+            return x
+
+    main_1_n = Before["main_1"].params[0].ty.shape[1]
+    After = relax.transform.BindSymbolicVars({main_1_n: 16})(Before)
+    tvm.ir.assert_structural_equal(Expected, After)
+
+
+def test_bind_single_variable_by_function_name():
+    """Variable name and function name may be used to replace a specific var"""
+
+    @tvm.script.ir_module
+    class Before:
+        @R.function
+        def main_1(x: R.Tensor(("m", "n"), dtype="float32")):
+            return x
+
+        @R.function
+        def main_2(x: R.Tensor(("m", "n"), dtype="float32")):
+            return x
+
+    @tvm.script.ir_module
+    class Expected:
+        @R.function
+        def main_1(x: R.Tensor(("m", 16), dtype="float32")):
+            return x
+
+        @R.function
+        def main_2(x: R.Tensor(("m", "n"), dtype="float32")):
+            return x
+
+    After = relax.transform.BindSymbolicVars({"n": 16}, "main_1")(Before)
+    tvm.ir.assert_structural_equal(Expected, After)
+
+
+def test_error_for_unused_replacement():
+    """Each replacement must be used"""
+
+    @tvm.script.ir_module
+    class Before:
+        @R.function
+        def main(x: R.Tensor(("m", "n"), dtype="float32")):
+            return x
+
+    with pytest.raises(RuntimeError):
+        relax.transform.BindSymbolicVars({"non_existing_var_name": 16})(Before)
+
+
+if __name__ == "__main__":
+    tvm.testing.main()