wehub-resource-sync
82 lines
2.5 KiB
Python
82 lines
2.5 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_take():
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@R.function
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def foo(x: R.Tensor((2, 3, 4), "float32"), indices: R.Tensor((3,), "int64")) -> R.Tensor(
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(2, 3, 3), "float32"
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):
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gv: R.Tensor((2, 3, 3), "float32") = R.take(x, indices, axis=2)
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return gv
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x = relax.Var("x", R.Tensor((2, 3, 4), "float32"))
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indices = relax.Var("indices", R.Tensor((3,), "int64"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x, indices]):
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gv = bb.emit(relax.op.take(x, indices, axis=2))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_strided_slice():
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@R.function
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def foo(x: R.Tensor((8, 9, 10, 10), "float32")) -> R.Tensor((4, 9, 10, 3), "float32"):
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gv: R.Tensor((4, 9, 10, 3), "float32") = R.strided_slice(
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x,
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axes=[0, 1, -1],
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begin=[1, 0, 8],
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end=[8, 9, 0],
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strides=[2, 1, -3],
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)
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return gv
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bb = relax.BlockBuilder()
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x = relax.Var("x", R.Tensor((8, 9, 10, 10), "float32"))
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with bb.function("foo", [x]):
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gv = bb.emit(
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relax.op.strided_slice(
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x, axes=[0, 1, -1], begin=[1, 0, 8], end=[8, 9, 0], strides=[2, 1, -3]
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)
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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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