wehub-resource-sync
107 lines
3.6 KiB
Python
107 lines
3.6 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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import tvm
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import tvm.relax
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import tvm.testing
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from tvm.relax.transform import KillAfterLastUse
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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 test_basic():
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@I.ir_module
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class Before:
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@R.function(pure=False)
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def main(x: R.Tensor([16, 32], "float32")):
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storage = R.memory.alloc_storage(R.shape([2048]), 0, "global", "uint8")
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y = R.memory.alloc_tensor(storage, 0, R.shape([16, 32]), "float32")
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_dummy = R.call_packed("add_tensors", [x, y], ty_args=(R.Tuple,))
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z = R.add(x, y)
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return z
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@I.ir_module
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class Expected:
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@R.function(pure=False)
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def main(x: R.Tensor([16, 32], "float32")):
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storage = R.memory.alloc_storage(R.shape([2048]), 0, "global", "uint8")
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y = R.memory.alloc_tensor(storage, 0, R.shape([16, 32]), "float32")
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_ = R.memory.kill_storage(storage)
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_dummy = R.call_packed("add_tensors", [x, y], ty_args=(R.Tuple,))
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z = R.add(x, y)
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_ = R.memory.kill_tensor(y)
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return z
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After = KillAfterLastUse()(Before)
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tvm.ir.assert_structural_equal(Expected, After)
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def test_track_usage_across_trivial_rebindings():
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"""To work around VM de-duplication of register usage"""
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@I.ir_module
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class Before:
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@R.function(pure=False)
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def main(w: R.Tensor([16, 32], "float32")):
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x = R.add(w, R.const(1, "float32"))
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y = x
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z = R.add(y, R.const(1, "float32"))
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return z
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@I.ir_module
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class Expected:
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@R.function(pure=False)
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def main(w: R.Tensor([16, 32], "float32")):
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x = R.add(w, R.const(1, "float32"))
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z = R.add(x, R.const(1, "float32"))
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_ = R.memory.kill_tensor(x)
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return z
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After = KillAfterLastUse()(Before)
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tvm.ir.assert_structural_equal(Expected, After)
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def test_track_usage_across_trivial_rebindings_in_match_cast():
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"""To work around VM de-duplication of register usage"""
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@I.ir_module
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class Before:
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@R.function(pure=False)
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def main(w: R.Tensor([16, 32], "float32")):
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x = R.add(w, R.const(1, "float32"))
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y = R.match_cast(x, R.Tensor([16, 32]))
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z = R.add(y, R.const(1, "float32"))
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return z
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@I.ir_module
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class Expected:
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@R.function(pure=False)
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def main(w: R.Tensor([16, 32], "float32")):
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x = R.add(w, R.const(1, "float32"))
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y = R.match_cast(x, R.Tensor([16, 32]))
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_ = R.memory.kill_tensor(x)
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z = R.add(y, R.const(1, "float32"))
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_ = R.memory.kill_tensor(y)
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return z
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After = KillAfterLastUse()(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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