Lunderberg commented on code in PR #16116: URL: https://github.com/apache/tvm/pull/16116#discussion_r1411574306
########## tests/python/relax/test_transform_remove_unused_parameters.py: ########## @@ -0,0 +1,101 @@ +# 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, relax as R, tir as T + + +class BaseCompare(tvm.testing.CompareBeforeAfter): + transform = tvm.relax.transform.RemoveUnusedParameters() + + +class TestSimple(BaseCompare): + @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 + + +class TestSymbolicVariables(BaseCompare): + @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.prim_value(n), R.prim_value(m)) + return out + + @R.function(private=True) + def func( + param_n: R.Prim(value="n"), param_m: R.Prim(value="m") + ) -> R.Tensor(["m", "n"], "float32"): + m = T.int64() + n = T.int64() + return R.zeros(R.shape([m, n]), dtype="float32") + + +class TestNoExtraSymbolicVariables(BaseCompare): + """Don't add symbolic variables if they can be inferred.""" + + @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 + Review Comment: Hmm. Currently, it only looks for callees that are represented by a `GlobalVar`, so it would ignore inner functions as they are represented by a `relax::Var`. When an inner function is being bound, it would be recursively visited, and that would include making updates to any `Call` nodes encountered inside. I'll need to add a test for it, but I think it would produce a well-formed output when a private function is called from within an inner function, but wouldn't remove unused parameters that are being used to call an inner function. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
