wehub-resource-sync
87 lines
2.9 KiB
Python
87 lines
2.9 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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import tvm
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import tvm.testing
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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_prim_value_in_assert_condition():
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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(A: R.Tensor(["N"])):
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N = T.int64()
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_ = R.assert_op(N % 16 == 0)
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return A
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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(A: R.Tensor(["N"])):
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N = T.int64()
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condition: R.Prim("bool") = Expected.compute_symbolic_expr(R.prim_value(N))
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_ = R.assert_op(condition)
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return A
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@T.prim_func(private=True, s_tir=True)
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def compute_symbolic_expr(N: T.int64) -> T.bool:
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T.func_attr({"tirx.is_host_func": True})
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T.ret(N % 16 == 0)
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After = tvm.relax.transform.ComputePrimValue()(Before)
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tvm.ir.assert_structural_equal(After, Expected)
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def test_prim_value_in_branch_condition():
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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(A: R.Tensor(["N"])):
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N = T.int64()
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if R.prim_value(N % 16 == 0):
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out = R.call_packed("fast_vectorized_impl", A, ty_args=[A.ty])
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else:
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out = R.call_packed("slow_non_vectorized_impl", A, ty_args=[A.ty])
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return out
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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(A: R.Tensor(["N"])):
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N = T.int64()
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condition: R.Prim("bool") = Expected.compute_symbolic_expr(R.prim_value(N))
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if condition:
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out = R.call_packed("fast_vectorized_impl", A, ty_args=[A.ty])
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else:
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out = R.call_packed("slow_non_vectorized_impl", A, ty_args=[A.ty])
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return out
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@T.prim_func(private=True, s_tir=True)
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def compute_symbolic_expr(N: T.int64) -> T.bool:
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T.func_attr({"tirx.is_host_func": True})
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T.ret(N % 16 == 0)
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After = tvm.relax.transform.ComputePrimValue()(Before)
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tvm.ir.assert_structural_equal(After, Expected)
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if __name__ == "__main__":
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tvm.testing.main()
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