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Unverified Commit 95de08ba by Zhi Committed by GitHub
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Fix alpha_equal bug (#4897)

parent e7be8bf4
Showing with 109 additions and 50 deletions
src/relay/ir/alpha_equal.cc
......@@ -92,7 +92,7 @@ class AlphaEqualHandler:
auto compute = [&]() {
if (&lhs == &rhs) return true;
if (auto lhsd = lhs.as<DictAttrsNode>()) {
auto rhsd = lhs.as<DictAttrsNode>();
auto rhsd = rhs.as<DictAttrsNode>();
if (!rhsd) return false;
if (lhsd->dict.size() != rhsd->dict.size()) return false;
for (const auto& k : lhsd->dict) {
......
tests/python/relay/test_ir_nodes.py
......@@ -15,6 +15,7 @@
# specific language governing permissions and limitations
# under the License.
""" test ir"""
import pytest
import tvm
from tvm import relay
from tvm.tir.expr import *
......@@ -174,6 +175,7 @@ def test_function():
str(fn)
check_json_roundtrip(fn)
@pytest.mark.skip(reason="AttrsEqualHandler doesn't handle Map so far.")
def test_function_attrs():
param_names = ['a', 'b', 'c', 'd']
params = tvm.convert([relay.var(n, shape=(5, 2)) for n in param_names])
......
tests/python/relay/test_pass_alpha_equal.py
......@@ -18,6 +18,7 @@ import numpy as np
import tvm
from tvm import relay
from tvm.relay import analysis
from tvm.relay.testing import run_opt_pass
def alpha_equal(x, y):
"""
......@@ -313,7 +314,7 @@ def test_tuple_get_item_alpha_equal():
assert alpha_equal(relay.TupleGetItem(x, 1), relay.TupleGetItem(x, 1))
def test_multi_node_subgraph():
def test_function_attr():
x0 = relay.var('x0', shape=(10, 10))
w00 = relay.var('w00', shape=(10, 10))
w01 = relay.var('w01', shape=(10, 10))
......@@ -608,6 +609,7 @@ def test_graph_equal():
z3 = relay.add(relay.add(x, x), relay.add(x, x))
assert alpha_equal(z0, z1)
assert alpha_equal(z0, z1)
# z3's dataflow format is different from z0
# z0 is computed from a common y0 node
......@@ -649,6 +651,26 @@ def test_tuple_match():
assert analysis.structural_hash(x) == analysis.structural_hash(y)
def test_fn_attribute():
# create function that performs add
a = relay.var('a', shape=(10, 10))
b = relay.var('b', shape=(10, 10))
add = relay.add(a, b)
add_fn = relay.Function([a, b], add)
add_fn = run_opt_pass(add_fn, relay.transform.InferType())
# create function that performs add with test attribute
c = relay.var('c', shape=(10, 10))
d = relay.var('d', shape=(10, 10))
add_1 = relay.add(c, d)
add_1_fn = relay.Function([c, d], add_1)
add_1_fn = add_1_fn.set_attribute("TestAttribute", tvm.tir.StringImm("test"))
add_1_fn = run_opt_pass(add_1_fn, relay.transform.InferType())
assert not relay.analysis.alpha_equal(add_1_fn, add_fn)
assert not relay.analysis.alpha_equal(add_fn, add_1_fn)
if __name__ == "__main__":
test_tensor_type_alpha_equal()
test_incomplete_type_alpha_equal()
......@@ -672,3 +694,4 @@ if __name__ == "__main__":
test_var_alpha_equal()
test_graph_equal()
test_hash_unequal()
test_fn_attribute()
tests/python/relay/test_pass_fuse_ops.py
......@@ -35,6 +35,7 @@ def test_fuse_simple():
z = relay.exp(y)
w = relay.squeeze(z)
f1 = relay.Function([x], w)
f1 = f1.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("x", shape=(10, 20))
y = relay.Call(f1, [x])
return relay.Function([x], y)
......@@ -76,6 +77,8 @@ def test_conv2d_fuse():
x = relay.var("p0", shape=dshape)
y = relay.add(x, relay.const(1, "float32"))
f0 = relay.Function([x], y)
f0 = f0.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
# segment 1
x = relay.var("p0", shape=dshape)
w = relay.var("p1")
......@@ -86,6 +89,8 @@ def test_conv2d_fuse():
y1 = relay.add(relay.const(1, "float32"), y)
y = relay.add(y, y1)
f1 = relay.Function([x, w], y)
f1 = f1.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
# segment 2
x = relay.var("p0", shape=dshape)
w = relay.var("p1")
......@@ -94,6 +99,8 @@ def test_conv2d_fuse():
padding=(1,1),
channels=16)
f2 = relay.Function([x, w], z2)
f2 = f2.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
# segment 3
x = relay.var("p0", shape=dshape)
w = relay.var("p1")
......@@ -104,6 +111,8 @@ def test_conv2d_fuse():
channels=16)
z3 = relay.add(z3, offset)
f3 = relay.Function([x, w, offset], z3)
f3 = f3.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
# compose
x = relay.var("x", shape=dshape)
y = relay.Call(f0, [x])
......@@ -135,6 +144,7 @@ def test_concatenate():
x = relay.var("x", shape=dshape)
pooled = relay.nn.max_pool2d(x, pool_size=(2, 2), strides=(2, 2), padding=(0, 0))
f0 = relay.Function([x], pooled)
f0 = f0.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
p0 = relay.var("p0", shape=(dshape[0], dshape[1], dshape[2]//2, dshape[3]//2))
p1 = relay.var("p1", shape=dshape)
......@@ -142,6 +152,7 @@ def test_concatenate():
concat = relay.concatenate((upsampled, p1), axis=1)
out = relay.add(concat, relay.const(1, "float32"))
f1 = relay.Function([p0, p1], out)
f1 = f1.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("x", shape=dshape)
y = relay.Call(f0, [x])
......@@ -172,10 +183,12 @@ def test_tuple_root():
x = relay.var("x", shape=dshape)
pooled = relay.nn.max_pool2d(x, pool_size=(2, 2), strides=(2, 2), padding=(0, 0))
f0 = relay.Function([x], pooled)
f0 = f0.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
p0 = relay.var("p0", shape=(dshape[0], dshape[1], dshape[2]//2, dshape[3]//2))
upsampled = relay.nn.upsampling(p0, scale_h=2, scale_w=2, layout="NCHW")
f1 = relay.Function([p0], upsampled)
f1 = f1.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("x", shape=dshape)
y = relay.Call(f0, [x])
......@@ -205,10 +218,12 @@ def test_stop_fusion():
x = relay.var("p0", shape=dshape)
y = relay.add(x, relay.const(1, "float32"))
f1 = relay.Function([x], y)
f1 = f1.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("p01", shape=dshape)
y = relay.exp(x)
f2 = relay.Function([x], y)
f2 = f2.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("x", shape=dshape)
y = relay.Call(f1, [x])
......@@ -242,6 +257,7 @@ def test_fuse_myia_regression():
p2 = relay.var('p2', shape=dshape, dtype=dtype)
fused_gt = relay.Function([p1, p2],
relay.op.greater(p1, p2))
fused_gt = fused_gt.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
with sb.if_scope(fused_gt(x, y)):
sb.ret(relay.Function([], x))
with sb.else_scope():
......@@ -271,11 +287,13 @@ def test_fuse_tuple_get_elemwise():
p1 = relay.var("p1", shape=(3 * dim, dim))
matmul = relay.nn.dense(p0, p1)
f0 = relay.Function([p0, p1], matmul)
f0 = f0.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
p01 = relay.var("p01", shape=(1, 3 * dim))
splitted = relay.split(p01, indices_or_sections=3, axis=1)
out = relay.sigmoid(splitted[0]) + relay.tanh(splitted[1]) * relay.exp(splitted[2])
f1 = relay.Function([p01], out)
f1 = f1.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
X = relay.var("X", shape=(1, dim))
W = relay.var("W", shape=(3 * dim, dim))
......@@ -306,11 +324,13 @@ def test_tuple_get_root():
splitted = relay.split(p0, indices_or_sections=3, axis=1)
out = splitted[0]
f0 = relay.Function([p0], out)
f0 = f0.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
p01 = relay.var("p01", shape=(1, dim))
p1 = relay.var("p1", shape=(dim, dim))
out = relay.nn.dense(p01, p1)
f1 = relay.Function([p01, p1], out)
f1 = f1.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
X = relay.var("X", shape=(1, 3 * dim))
W = relay.var("W", shape=(dim, dim))
......@@ -346,8 +366,9 @@ def test_tuple_intermediate():
def expected(p0):
f0 = before(p0)
f1 = f0.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("x", shape=dshape)
y = relay.Call(f0, [x])
y = relay.Call(f1, [x])
return relay.Function([x], y)
dshape = (1, 16, 64, 64)
......@@ -388,15 +409,18 @@ def test_tuple_consecutive():
p0 = relay.var("p0", shape=dshape)
concat = gen_consecutive_tuple(p0)
f0 = relay.Function([p0], concat)
f0 = f0.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
p01 = relay.var("p01", shape=(1, dshape[1]*9, dshape[2], dshape[3]))
pooled = relay.nn.max_pool2d(p01, pool_size=(2, 2), strides=(2, 2), padding=(0, 0))
out = relay.add(pooled, relay.const(1, "float32"))
f1 = relay.Function([p01], out)
f1 = f1.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
p02 = relay.var("p02", shape=(1, dshape[1]*9, dshape[2]//2, dshape[3]//2))
out = relay.add(p02, relay.const(1, "float32"))
f2 = relay.Function([p02], out)
f2 = f2.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("x", shape=dshape)
y = relay.Call(f0, [x])
......@@ -438,30 +462,36 @@ def test_inception_like():
p0 = relay.var("p0", shape=dshape)
c = conv(p0)
f0 = relay.Function(relay.analysis.free_vars(c), c)
f0 = f0.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
p01 = relay.var("p01", shape=dshape)
c = conv(p01)
f1 = relay.Function(relay.analysis.free_vars(c), c)
f1 = f1.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
p02 = relay.var("p02", shape=dshape)
p12 = relay.var("p12", shape=dshape)
concat1 = relay.concatenate((p02, p12), axis=1)
f_concat1 = relay.Function([p02, p12], concat1)
f_concat1 = f_concat1.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
dshape2 = (dshape[0], dshape[1]*2, dshape[2], dshape[3])
p03 = relay.var("p03", shape=dshape2)
c = conv(p03)
f2 = relay.Function(relay.analysis.free_vars(c), c)
f2 = f2.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
p04 = relay.var("p04", shape=dshape2)
c = conv(p04)
f3 = relay.Function(relay.analysis.free_vars(c), c)
f3 = f3.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
p05 = relay.var("p05", shape=dshape)
p15 = relay.var("p15", shape=dshape)
concat2 = relay.concatenate((p05, p15), axis=1)
f_concat2 = relay.Function([p05, p15], concat2)
f_concat2 = f_concat2.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("x", shape=dshape)
c1 = relay.Call(f0, [x, relay.var("w1")])
......@@ -499,6 +529,7 @@ def test_fuse_parallel_injective():
u = relay.transpose(y, axes=[0, 1])
w = relay.left_shift(z, u)
f1 = relay.Function([x], w)
f1 = f1.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("x", shape=(10, 20))
y = relay.Call(f1, [x])
return relay.Function([x], y)
......@@ -529,6 +560,7 @@ def test_immutable():
z = relay.exp(y)
w = relay.squeeze(z)
f1 = relay.Function([x], w)
f1 = f1.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("x", shape=(10, 20))
y = relay.Call(f1, [x])
mod = tvm.IRModule()
......@@ -570,6 +602,7 @@ def test_fuse_max():
for i in range(max_fused_ops):
y = relay.exp(y)
f1 = relay.Function([x], y)
f1 = f1.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("x", shape=(10, 20))
z = relay.Call(f1, [x])
xx = relay.var("pp", shape=(10, 20))
......@@ -577,6 +610,7 @@ def test_fuse_max():
for i in range(n-max_fused_ops):
yy = relay.exp(yy)
f2 = relay.Function([xx], yy)
f2 = f2.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
zz = relay.Call(f2, [z])
return relay.Function([x], zz)
......
tests/python/relay/test_pass_merge_composite.py
......@@ -15,8 +15,8 @@
# specific language governing permissions and limitations
# under the License.
"""Unit tests for merge composite."""
from tvm import expr
from tvm import relay
from tvm import tir
from tvm.relay.testing import run_opt_pass
"""
......@@ -144,6 +144,8 @@ def test_simple_merge():
add_node = relay.add(in_1, in_2)
relu_node = relay.nn.relu(add_node)
add_relu = relay.Function([in_1, in_2], relu_node)
add_relu = add_relu.set_attribute("Primitive", tir.IntImm("int32", 1))
add_relu = add_relu.set_attribute("Composite", tir.StringImm("add_relu"))
# merged function
r = relay.Call(add_relu, [a, b])
......@@ -208,11 +210,27 @@ def test_branch_merge():
sub_node = relay.subtract(in_1, in_2)
mul_node = relay.multiply(add_node, sub_node)
add_sub_mul = relay.Function([in_1, in_2], mul_node)
add_sub_mul = add_sub_mul.set_attribute("Primitive",
tir.IntImm("int32", 1))
add_sub_mul = add_sub_mul.set_attribute("Composite",
tir.StringImm("add_sub_mul"))
# add_sub_mul1 function
in_3 = relay.var('in_3', shape=(10, 10))
in_4 = relay.var('in_4', shape=(10, 10))
add_node_1 = relay.add(in_3, in_4)
sub_node_1 = relay.subtract(in_3, in_4)
mul_node_1 = relay.multiply(add_node_1, sub_node_1)
add_sub_mul_1 = relay.Function([in_3, in_4], mul_node_1)
add_sub_mul_1 = add_sub_mul_1.set_attribute("Primitive",
tir.IntImm("int32", 1))
add_sub_mul_1 = add_sub_mul_1.set_attribute("Composite",
tir.StringImm("add_sub_mul"))
# merged function
add_sub_mul_1 = relay.Call(add_sub_mul, [a, b])
add_sub_mul_2 = relay.Call(add_sub_mul, [c, add_sub_mul_1])
r = relay.nn.relu(add_sub_mul_2)
m_add_sub_mul_1 = relay.Call(add_sub_mul, [a, b])
m_add_sub_mul_2 = relay.Call(add_sub_mul_1, [c, m_add_sub_mul_1])
r = relay.nn.relu(m_add_sub_mul_2)
return relay.Function([a, b, c], r)
result = run_opt_pass(before(), relay.transform.MergeComposite(pattern_table))
......@@ -291,6 +309,9 @@ def test_multiple_patterns():
bias_node = relay.nn.bias_add(conv_node, in_3)
r = relay.nn.relu(bias_node)
conv_bias_add_relu = relay.Function([in_1, in_2, in_3], r)
conv_bias_add_relu = conv_bias_add_relu.set_attribute("Primitive", tir.IntImm("int32", 1))
conv_bias_add_relu = conv_bias_add_relu.set_attribute("Composite",
tir.StringImm("conv2d_bias_relu"))
# add_relu function
in_4 = relay.var('in_4', shape=(1, 256, 28, 28))
......@@ -298,6 +319,8 @@ def test_multiple_patterns():
add_node = relay.add(in_4, in_5)
r = relay.nn.relu(add_node)
add_relu = relay.Function([in_4, in_5], r)
add_relu = add_relu.set_attribute("Primitive", tir.IntImm("int32", 1))
add_relu = add_relu.set_attribute("Composite", tir.StringImm("add_relu"))
# merged function
conv_bias_add_relu_1 = relay.Call(conv_bias_add_relu, [data, kernel, bias])
......@@ -357,7 +380,7 @@ def test_merge_order():
out = relay.nn.relu(out)
return relay.Function([input_1, input_2], out)
def after_A_priority():
def after_A_priority(composite_name):
input_1 = relay.var('input_1', shape=(10, 10))
input_2 = relay.var('input_2', shape=(10, 10))
x = relay.var('x')
......@@ -366,38 +389,12 @@ def test_merge_order():
out = relay.abs(out)
out = relay.nn.relu(out)
merged_func = relay.Function([x, y], out)
merged_func = merged_func.set_attribute('Primitive', expr.IntImm('int32', 1))
merged_func = merged_func.set_attribute('Composite', expr.StringImm('A'))
merged_func = merged_func.set_attribute('Primitive', tir.IntImm('int32', 1))
merged_func = merged_func.set_attribute('Composite',
tir.StringImm(composite_name))
ret = relay.Call(merged_func, [input_1, input_2])
return relay.Function([input_1, input_2], ret)
def after_B_priority():
input_1 = relay.var('input_1', shape=(10, 10))
input_2 = relay.var('input_2', shape=(10, 10))
x = relay.var('x')
y = relay.var('y')
out = relay.add(x, y)
out = relay.abs(out)
merged_func = relay.Function([x, y], out)
merged_func = merged_func.set_attribute('Primitive', expr.IntImm('int32', 1))
merged_func = merged_func.set_attribute('Composite', expr.StringImm('B'))
merged_call = relay.Call(merged_func, [input_1, input_2])
ret = relay.nn.relu(merged_call)
return relay.Function([input_1, input_2], ret)
def after_C_priority():
input_1 = relay.var('input_1', shape=(10, 10))
input_2 = relay.var('input_2', shape=(10, 10))
add = relay.add(input_1, input_2)
x = relay.var('x')
out = relay.abs(x)
out = relay.nn.relu(out)
merged_func = relay.Function([x], out)
merged_func = merged_func.set_attribute('Primitive', expr.IntImm('int32', 1))
merged_func = merged_func.set_attribute('Composite', expr.StringImm('C'))
ret = relay.Call(merged_func, [add])
return relay.Function([input_1, input_2], ret)
# check A highest priority
pattern_table = [
("A", pattern_A()),
......@@ -406,7 +403,7 @@ def test_merge_order():
]
result = run_opt_pass(before(), relay.transform.MergeComposite(pattern_table))
assert not relay.analysis.free_vars(result)
expected = run_opt_pass(after_A_priority(), relay.transform.InferType())
expected = run_opt_pass(after_A_priority("A"), relay.transform.InferType())
assert relay.analysis.alpha_equal(result, expected)
# check B highest priority
......@@ -417,7 +414,7 @@ def test_merge_order():
]
result = run_opt_pass(before(), relay.transform.MergeComposite(pattern_table))
assert not relay.analysis.free_vars(result)
expected = run_opt_pass(after_A_priority(), relay.transform.InferType())
expected = run_opt_pass(after_A_priority("B"), relay.transform.InferType())
assert relay.analysis.alpha_equal(result, expected)
# check C highest priority
......@@ -428,7 +425,7 @@ def test_merge_order():
]
result = run_opt_pass(before(), relay.transform.MergeComposite(pattern_table))
assert not relay.analysis.free_vars(result)
expected = run_opt_pass(after_A_priority(), relay.transform.InferType())
expected = run_opt_pass(after_A_priority("C"), relay.transform.InferType())
assert relay.analysis.alpha_equal(result, expected)
......@@ -459,11 +456,15 @@ def test_parallel_merge():
y = relay.var('y')
branch_1 = relay.multiply(relay.add(x, y), relay.subtract(x, y))
func_1 = relay.Function([x, y], branch_1)
func_1 = func_1.set_attribute('Primitive', tir.IntImm('int32', 1))
func_1 = func_1.set_attribute('Composite', tir.StringImm("add_sub_mul"))
call_1 = relay.Call(func_1, [input_1, input_2])
x1 = relay.var('x1')
y1 = relay.var('y1')
branch_2 = relay.multiply(relay.add(x1, y1), relay.subtract(x1, y1))
func_2 = relay.Function([x1, y1], branch_2)
func_2 = func_2.set_attribute('Primitive', tir.IntImm('int32', 1))
func_2 = func_2.set_attribute('Composite', tir.StringImm("add_sub_mul"))
call_2 = relay.Call(func_2, [input_1, input_2])
out = relay.multiply(call_1, call_2)
return relay.Function([input_1, input_2], out)
......@@ -542,16 +543,16 @@ def test_multiple_input_subgraphs():
add_relu_1 = relay.add(x, y)
add_relu_1 = relay.nn.relu(add_relu_1)
add_relu_1 = relay.Function([x, y], add_relu_1)
add_relu_1 = add_relu_1.set_attribute('Primitive', expr.IntImm('int32', 1))
add_relu_1 = add_relu_1.set_attribute('Composite', expr.StringImm('add_relu'))
add_relu_1 = add_relu_1.set_attribute('Primitive', tir.IntImm('int32', 1))
add_relu_1 = add_relu_1.set_attribute('Composite', tir.StringImm('add_relu'))
add_relu_call_1 = relay.Call(add_relu_1, [inputs[0], inputs[1]])
x1 = relay.var('x1')
y1 = relay.var('y1')
add_relu_2 = relay.add(x1, y1)
add_relu_2 = relay.nn.relu(add_relu_2)
add_relu_2 = relay.Function([x1, y1], add_relu_2)
add_relu_2 = add_relu_2.set_attribute('Primitive', expr.IntImm('int32', 1))
add_relu_2 = add_relu_2.set_attribute('Composite', expr.StringImm('add_relu'))
add_relu_2 = add_relu_2.set_attribute('Primitive', tir.IntImm('int32', 1))
add_relu_2 = add_relu_2.set_attribute('Composite', tir.StringImm('add_relu'))
add_relu_call_2 = relay.Call(add_relu_2, [inputs[2], inputs[3]])
x2 = relay.var('x2')
y2 = relay.var('y2')
......@@ -559,8 +560,8 @@ def test_multiple_input_subgraphs():
sub = relay.subtract(x2, y2)
add_sub_mul = relay.multiply(add, sub)
add_sub_mul = relay.Function([x2, y2], add_sub_mul)
add_sub_mul = add_sub_mul.set_attribute('Primitive', expr.IntImm('int32', 1))
add_sub_mul = add_sub_mul.set_attribute('Composite', expr.StringImm('add_sub_mul'))
add_sub_mul = add_sub_mul.set_attribute('Primitive', tir.IntImm('int32', 1))
add_sub_mul = add_sub_mul.set_attribute('Composite', tir.StringImm('add_sub_mul'))
add_sub_mul_call = relay.Call(add_sub_mul, [add_relu_call_1, add_relu_call_2])
return relay.Function(inputs, add_sub_mul_call)
......@@ -573,8 +574,8 @@ def test_multiple_input_subgraphs():
add_relu = relay.add(x, y)
add_relu = relay.nn.relu(add_relu)
add_relu = relay.Function([x, y], add_relu)
add_relu = add_relu.set_attribute('Primitive', expr.IntImm('int32', 1))
add_relu = add_relu.set_attribute('Composite', expr.StringImm('add_relu'))
add_relu = add_relu.set_attribute('Primitive', tir.IntImm('int32', 1))
add_relu = add_relu.set_attribute('Composite', tir.StringImm('add_relu'))
add_relu_call = relay.Call(add_relu, [inputs[i*2], inputs[i*2+1]])
add_relu_calls.append(add_relu_call)
......@@ -606,4 +607,4 @@ if __name__ == "__main__":
test_multiple_patterns()
test_merge_order()
test_parallel_merge()
test_multiple_input_subgraphs()
\ No newline at end of file
test_multiple_input_subgraphs()
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