test_pass_eliminate_common_subexpr.py 2.83 KB
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
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"""Test eliminate common subexpr pass"""
from tvm import relay
from tvm.relay.op import register_alter_op_layout
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from tvm.relay import transform, analysis


def run_opt_pass(expr, opt_pass):
    assert isinstance(opt_pass, transform.Pass)
    mod = relay.Module.from_expr(expr)
    mod = opt_pass(mod)
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    entry = mod["main"]
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    return entry if isinstance(expr, relay.Function) else entry.body
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def test_simple():
    def before():
        x = relay.var("x", shape=(1, 16))
        y1 = relay.nn.relu(x)
        y2 = relay.nn.relu(x)
        y1 = relay.add(y1, relay.const(1.0, "float32"))
        y2 = relay.add(y2, relay.const(1.0, "float32"))
        y = relay.add(y1, y2)
        f = relay.Function([x], y)
        return f

    def expected():
        x = relay.var("x", shape=(1, 16))
        y = relay.nn.relu(x)
        y = relay.add(y, relay.const(1.0, "float32"))
        y = relay.add(y, y)
        f = relay.Function([x], y)
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        return run_opt_pass(f, transform.InferType())
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    z = before()
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    z = run_opt_pass(z, transform.EliminateCommonSubexpr())
    assert analysis.alpha_equal(z, expected())
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def test_callback():
    def before():
        x = relay.var("x", shape=(1, 16))
        y1 = relay.nn.relu(x)
        y2 = relay.nn.relu(x)
        y1 = relay.add(y1, relay.const(1.0, "float32"))
        y2 = relay.add(y2, relay.const(1.0, "float32"))
        y = relay.add(y1, y2)
        f = relay.Function([x], y)
        return f

    def expected():
        x = relay.var("x", shape=(1, 16))
        y = relay.nn.relu(x)
        y1 = relay.add(y, relay.const(1.0, "float32"))
        y2 = relay.add(y, relay.const(1.0, "float32"))
        y = relay.add(y1, y2)
        f = relay.Function([x], y)
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        return run_opt_pass(f, transform.InferType())
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    def fskip(expr):
        if isinstance(expr, relay.expr.Call) and expr.op.name == 'add':
            return True
        return False

    z = before()
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    z = run_opt_pass(z, transform.EliminateCommonSubexpr(fskip))
    assert analysis.alpha_equal(z, expected())
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if __name__ == "__main__":
    test_simple()
    test_callback()