wehub-resource-sync
107 lines
3.3 KiB
Python
107 lines
3.3 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# ruff: noqa: F401
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import pytest
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import tvm
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import tvm.script
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import tvm.testing
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from tvm import relax
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from tvm.script import ir as I
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from tvm.script import relax as R
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from tvm.script import tirx as T
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def test_inline_simple():
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"""Simple case of inlining
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Inlining applies to all private functions
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"""
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@I.ir_module
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class Before:
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@R.function
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def main(A: R.Tensor([16, 16], "int32")) -> R.Tensor([16, 32], "int32"):
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B = A * A
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C = Before.subroutine(B)
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D = C + C
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return D
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@R.function(private=True)
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def subroutine(B: R.Tensor([16, 16], "int32")) -> R.Tensor([16, 32], "int32"):
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C = R.concat([B, B], axis=1)
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return C
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@I.ir_module
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class Expected:
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@R.function
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def main(A: R.Tensor([16, 16], "int32")) -> R.Tensor([16, 32], "int32"):
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B = A * A
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C = R.concat([B, B], axis=1)
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D = C + C
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return D
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After = tvm.relax.transform.InlinePrivateFunctions()(Before)
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tvm.ir.assert_structural_equal(Expected, After)
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def test_skip_inline_of_recursive_functions():
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"""Recursively-defined functions
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This behavior is deliberately different between the
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`relax.transform.InlinePrivateFunctions` pass, and the
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`relax.Function.inline_functions` utility.
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For a user-facing utility, such as `func.inline_functions(...)`,
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the functions to be inlined are specifically listed, and must not
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be ignored. If it is unable to inline the user-requested
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function, it should return an appropriate error.
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For a generic utility to be used in optimization pipelines, the
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framework is tasked with selecting the functions to be inlined,
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and should avoid selecting any function that cannot be inlined.
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This includes recursively-defined functions.
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"""
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@I.ir_module
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class Before:
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@R.function
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def main():
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B = Before.subroutine()
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return B
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@R.function(private=True)
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def subroutine() -> R.Tensor([], "int64"):
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R.func_attr({"relax.force_pure": True})
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cond = R.call_packed("dummy_function", ty_args=R.Tensor([], "bool"))
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if cond:
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Out = Before.subroutine()
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else:
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Out = R.const(0, "int64")
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return Out
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Expected = Before
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After = tvm.relax.transform.InlinePrivateFunctions()(Before)
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tvm.ir.assert_structural_equal(Expected, After)
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if __name__ == "__main__":
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tvm.testing.main()
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