wehub-resource-sync
118 lines
3.9 KiB
Python
118 lines
3.9 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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"""
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Test relax transform - Eliminate redundant reshape operations
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"""
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import tvm.testing
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from tvm import relax
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from tvm.relax.transform import DeadCodeElimination, RemoveRedundantReshape
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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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def _run_pass_compare_output(Before, Expected):
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fused_mod = RemoveRedundantReshape()(Before)
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fused_mod = DeadCodeElimination()(fused_mod)
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tvm.ir.assert_structural_equal(Expected, fused_mod)
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def test_remove_redundant_reshape_pass_one_arg():
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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(x: R.Tensor((1, 1001, 1, 1), dtype="float16")) -> R.Tensor(
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(1, 1001), dtype="float16"
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):
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with R.dataflow():
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lv: R.Tensor((1, 1001), dtype="float16") = R.reshape(x, R.shape([1, 1001]))
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lv1: R.Tensor((1, 1001), dtype="float16") = R.reshape(lv, R.shape([1, 1001]))
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gv: R.Tensor((1, 1001), dtype="float16") = R.reshape(lv1, R.shape([1, 1001]))
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R.output(gv)
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return gv
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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(x: R.Tensor((1, 1001, 1, 1), dtype="float16")) -> R.Tensor(
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(1, 1001), dtype="float16"
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):
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with R.dataflow():
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gv: R.Tensor((1, 1001), dtype="float16") = R.reshape(x, R.shape([1, 1001]))
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R.output(gv)
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return gv
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_run_pass_compare_output(Before, Expected)
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def test_remove_redundant_reshape_pass_two_arg():
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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(x: R.Tensor((1, 1001, 1, 1), dtype="float16")) -> R.Tensor(
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(1, 1001), dtype="float16"
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):
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with R.dataflow():
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lv: R.Tensor((1, 1001, 1), dtype="float16") = R.reshape(x, R.shape([1, 1001, 1]))
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lv1: R.Tensor((1, 1001), dtype="float16") = R.reshape(lv, R.shape([1, 1001]))
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R.output(lv1)
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return lv1
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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(x: R.Tensor((1, 1001, 1, 1), dtype="float16")) -> R.Tensor(
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(1, 1001), dtype="float16"
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):
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with R.dataflow():
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lv1: R.Tensor((1, 1001), dtype="float16") = R.reshape(x, R.shape([1, 1001]))
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R.output(lv1)
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return lv1
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_run_pass_compare_output(Before, Expected)
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def test_remove_redundant_reshape_pass_three_arg():
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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(x: R.Tensor((1, 1001, 1, 1), dtype="float16")) -> R.Tensor(
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(1, 1001, 1, 1), dtype="float16"
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):
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with R.dataflow():
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lv: R.Tensor((1, 1001, 1, 1), dtype="float16") = R.reshape(
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x, R.shape([1, 1001, 1, 1])
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)
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R.output(lv)
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return lv
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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(x: R.Tensor((1, 1001, 1, 1), dtype="float16")) -> R.Tensor(
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(1, 1001, 1, 1), dtype="float16"
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):
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return x
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_run_pass_compare_output(Before, Expected)
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if __name__ == "__main__":
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tvm.testing.main()
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