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test_pass_legalize.py
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test_pass_legalize.py
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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.
"""Test legalize pass"""
import numpy as np
import tvm
from tvm import te
from tvm import relay
from tvm.contrib import graph_runtime
from tvm.relay import transform, analysis
from tvm.relay.testing.temp_op_attr import TempOpAttr
def run_opt_pass(expr, passes):
passes = passes if isinstance(passes, list) else [passes]
mod = tvm.IRModule.from_expr(expr)
seq = tvm.transform.Sequential(passes)
with tvm.transform.PassContext(opt_level=3):
mod = seq(mod)
entry = mod["main"]
return entry if isinstance(expr, relay.Function) else entry.body
def test_legalize():
"""Test directly replacing an operator with a new one"""
def before():
x = relay.var("x", shape=(1, 64, 56, 56))
weight = relay.var("weight", shape=(64, 64, 3, 3))
y = relay.nn.conv2d(x, weight, channels=64, kernel_size=(3, 3), padding=(1, 1))
y = relay.nn.relu(y)
y = relay.Function([x, weight], y)
return y
def legalize_conv2d(attrs, inputs, types):
data, weight = inputs
weight = relay.multiply(weight, relay.const(2.0, "float32"))
return relay.nn.conv2d(data, weight, **attrs)
def expected():
x = relay.var("x", shape=(1, 64, 56, 56))
weight = relay.var("weight", shape=(64, 64, 3, 3))
y = relay.nn.conv2d(
x,
relay.multiply(weight, relay.const(2.0, "float32")),
channels=64,
kernel_size=(3, 3),
padding=(1, 1),
)
y = relay.nn.relu(y)
y = relay.Function([x, weight], y)
return y
with TempOpAttr("nn.conv2d", "FTVMLegalize", legalize_conv2d):
a = before()
a = run_opt_pass(a, transform.Legalize())
b = run_opt_pass(expected(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
def test_legalize_none():
"""Test doing nothing by returning 'None' """
def before():
x = relay.var("x", shape=(1, 64, 56, 56))
y = relay.nn.global_max_pool2d(x)
y = relay.Function([x], y)
return y
called = [False]
def legalize_conv2d(attrs, inputs, types):
called[0] = True
return None
with TempOpAttr("nn.global_max_pool2d", "FTVMLegalize", legalize_conv2d):
a = before()
a = run_opt_pass(a, transform.Legalize())
b = run_opt_pass(before(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
assert called[0]
def test_legalize_multiple_ops():
"""Test directly replacing an operator with a new one"""
def before():
x = relay.var("x", shape=(1, 64, 56, 56))
weight = relay.var("weight", shape=(64, 64, 3, 3))
y = relay.nn.conv2d(x, weight, channels=64, kernel_size=(3, 3), padding=(1, 1))
y = relay.nn.relu(y)
y = relay.Function([x, weight], y)
return y
def legalize_conv2d(attrs, inputs, types):
data, weight = inputs
weight = relay.multiply(weight, relay.const(2.0, "float32"))
return relay.nn.conv2d(data, weight, **attrs)
def legalize_relu(attrs, inputs, types):
data = inputs[0]
add = relay.add(tvm.relay.const(0, "float32"), data)
return relay.nn.relu(add)
def expected():
x = relay.var("x", shape=(1, 64, 56, 56))
weight = relay.var("weight", shape=(64, 64, 3, 3))
y = relay.nn.conv2d(
x,
relay.multiply(weight, relay.const(2.0, "float32")),
channels=64,
kernel_size=(3, 3),
padding=(1, 1),
)
y = relay.add(tvm.relay.const(0, "float32"), y)
y = relay.nn.relu(y)
y = relay.Function([x, weight], y)
return y
with TempOpAttr("nn.conv2d", "FTVMLegalize", legalize_conv2d):
with TempOpAttr("nn.relu", "FTVMLegalize", legalize_relu):
a = before()
a = run_opt_pass(a, transform.Legalize())
b = run_opt_pass(expected(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
def test_legalize_multi_input():
"""Test directly replacing an operator with a new one"""
def before():
x = relay.var("x", shape=(1, 64, 56, 56))
y = relay.var("y", shape=(1, 64, 56, 20))
z = relay.var("z", shape=(1, 64, 56, 10))
func = relay.concatenate([x, y, z], axis=3)
func = relay.Function([x, y, z], func)
return func
def legalize_concatenate(attrs, inputs, types):
# Check that the correct multi-input case is handled.
assert len(inputs) == 1
assert isinstance(inputs[0], tvm.relay.expr.Tuple)
assert len(types) == 2
assert isinstance(types[0], tvm.relay.ty.TupleType)
assert isinstance(types[1], tvm.relay.ty.TensorType)
return None
def expected():
x = relay.var("x", shape=(1, 64, 56, 56))
y = relay.var("y", shape=(1, 64, 56, 20))
z = relay.var("z", shape=(1, 64, 56, 10))
func = relay.concatenate([x, y, z], axis=3)
func = relay.Function([x, y, z], func)
return func
with TempOpAttr("concatenate", "FTVMLegalize", legalize_concatenate):
a = before()
a = run_opt_pass(a, transform.Legalize())
b = run_opt_pass(expected(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
if __name__ == "__main__":
test_legalize()
test_legalize_none()
test_legalize_multiple_ops()
test_legalize_multi_input()