wehub-resource-sync
66 lines
2.1 KiB
Python
66 lines
2.1 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.script
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import tvm.testing
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from tvm import IRModule, relax
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from tvm.script import relax as R
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def _check(
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parsed: relax.Function | IRModule,
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expect: relax.Function | IRModule | None,
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):
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test = parsed.script(show_meta=True)
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roundtrip_mod = tvm.script.from_source(test)
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tvm.ir.assert_structural_equal(parsed, roundtrip_mod)
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if expect:
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tvm.ir.assert_structural_equal(parsed, expect)
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def test_unique():
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@R.function
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def foo(x: R.Tensor((2, 3, 4), dtype="float32")) -> R.Tuple(
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R.Tensor(dtype="float32", ndim=3),
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R.Tensor(dtype="int64", ndim=1),
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R.Tensor(dtype="int64", ndim=1),
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):
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gv: R.Tuple(
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R.Tensor(dtype="float32", ndim=3),
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R.Tensor(dtype="int64", ndim=1),
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R.Tensor(dtype="int64", ndim=1),
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) = R.unique(
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x, sorted=True, return_index=False, return_inverse=True, return_counts=True, axis=1
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)
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return gv
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x = relax.Var("x", R.Tensor((2, 3, 4), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(
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relax.op.unique(x, sorted=True, return_inverse=True, return_counts=True, axis=1)
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)
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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if __name__ == "__main__":
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tvm.testing.main()
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