chore: import upstream snapshot with attribution

tests/python/relax/test_transform_remove_unused_parameters.py

@@ -0,0 +1,223 @@
+# 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.testing
+from tvm.script import ir as I
+from tvm.script import relax as R
+from tvm.script import tirx as T
+
+
+def test_remove_unused_relax_parameter():
+    """A relax parameter may be removed
+
+    This is only allowed for internal function calls, where all
+    callsites can be updated.  For externally-exposed functions, the
+    signature may not be modified.
+    """
+
+    @I.ir_module
+    class Before:
+        @R.function
+        def main(A: R.Tensor, B: R.Tensor):
+            return Before.func(A, B)
+
+        @R.function(private=True)
+        def func(A: R.Tensor, B: R.Tensor) -> R.Tensor:
+            return A
+
+    @I.ir_module
+    class Expected:
+        @R.function
+        def main(A: R.Tensor, B: R.Tensor):
+            return Expected.func(A)
+
+        @R.function(private=True)
+        def func(A: R.Tensor) -> R.Tensor:
+            return A
+
+    After = tvm.relax.transform.RemoveUnusedParameters()(Before)
+    tvm.ir.assert_structural_equal(After, Expected)
+
+
+def test_replace_symbolic_variables():
+    """If a parameter is only required for its symbolic variables, provide them directly
+
+    The relax parameter `A` isn't used by the subroutine.  However,
+    its shape defines the symbolic variables `m` and `n`.  When
+    removing the `R.Tensor` argument, we may need to provide
+    additional parameters to define the symbolic variables.
+
+    A `PrimType` carries only a dtype and defines no TIR var.  Each free
+    symbolic variable is therefore promoted through a 1-D `R.Shape`
+    parameter, which actually *defines* the variable.
+    """
+
+    @I.ir_module
+    class Before:
+        @R.function
+        def main(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            return Before.func(A)
+
+        @R.function(private=True)
+        def func(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            return R.zeros(R.shape([m, n]), dtype="float32")
+
+    @I.ir_module
+    class Expected:
+        @R.function
+        def main(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            out: R.Tensor([m, n], "float32") = Expected.func(R.shape([n]), R.shape([m]))
+            return out
+
+        @R.function(private=True)
+        def func(param_n: R.Shape(["n"]), param_m: R.Shape(["m"])) -> R.Tensor(
+            ["m", "n"], "float32"
+        ):
+            m = T.int64()
+            n = T.int64()
+            return R.zeros(R.shape([m, n]), dtype="float32")
+
+    After = tvm.relax.transform.RemoveUnusedParameters()(Before)
+    tvm.ir.assert_structural_equal(After, Expected)
+
+
+def test_no_extra_symbolic_variables():
+    """Don't add symbolic variables if they can be inferred.
+
+    Even though some cases require adding new parameters to provide
+    symbolic variables, not every symbolic variable requires a
+    distinct parameter.
+    """
+
+    @I.ir_module
+    class Before:
+        @R.function
+        def main(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            return Before.func(A)
+
+        @R.function(private=True)
+        def func(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            zeros = R.zeros(R.shape([m, n]), dtype="float32")
+            out = R.add(A, zeros)
+            return out
+
+    Expected = Before
+
+    After = tvm.relax.transform.RemoveUnusedParameters()(Before)
+    tvm.ir.assert_structural_equal(After, Expected)
+
+
+def test_remove_extra_prim_parameters():
+    """Remove unused scalar parameters.
+
+    The tensor parameter already defines the symbolic dimensions, while the
+    dtype-only scalar parameters are unused by the private function.
+    """
+
+    @I.ir_module
+    class Before:
+        @R.function
+        def main(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            return Before.func(A, R.prim_value(m), R.prim_value(n))
+
+        @R.function(private=True)
+        def func(
+            A: R.Tensor(["m", "n"], "float32"),
+            _m: R.Prim("int64"),
+            _n: R.Prim("int64"),
+        ) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            zeros = R.zeros(R.shape([m, n]), dtype="float32")
+            out = R.add(A, zeros)
+            return out
+
+    @I.ir_module
+    class Expected:
+        @R.function
+        def main(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            return Expected.func(A)
+
+        @R.function(private=True)
+        def func(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            zeros = R.zeros(R.shape([m, n]), dtype="float32")
+            out = R.add(A, zeros)
+            return out
+
+    After = tvm.relax.transform.RemoveUnusedParameters()(Before)
+    tvm.ir.assert_structural_equal(After, Expected)
+
+
+def test_remove_extra_shape_variables():
+    """Remove parameters that only serve to define existing symbolic variables
+
+    If a `R.Shape` parameter provides a definition of a symbolic
+    variable, but that symbolic variable can be determined from a
+    different parameter, then the `R.Shape` parameter can be removed.
+    """
+
+    @I.ir_module
+    class Before:
+        @R.function
+        def main(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            return Before.func(A, R.shape([m, n]))
+
+        @R.function(private=True)
+        def func(
+            A: R.Tensor(["m", "n"], "float32"),
+            _: R.Shape(["m", "n"]),
+        ) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            zeros = R.zeros(R.shape([m, n]), dtype="float32")
+            out = R.add(A, zeros)
+            return out
+
+    @I.ir_module
+    class Expected:
+        @R.function
+        def main(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            return Expected.func(A)
+
+        @R.function(private=True)
+        def func(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            zeros = R.zeros(R.shape([m, n]), dtype="float32")
+            out = R.add(A, zeros)
+            return out
+
+    After = tvm.relax.transform.RemoveUnusedParameters()(Before)
+    tvm.ir.assert_structural_equal(After, Expected)
+
+
+if __name__ == "__main__":
+    tvm.testing.main()