Skip to content

Commit

Permalink
Fix alpha_equal bug (#4897)
Browse files Browse the repository at this point in the history
  • Loading branch information
@zhiics
zhiics authored Feb 17, 2020
1 parent e7be8bf commit 95de08b
Show file tree
Hide file tree
Showing 5 changed files with 109 additions and 49 deletions.
2 changes: 1 addition & 1 deletion src/relay/ir/alpha_equal.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -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) {
Expand Down
2 changes: 2 additions & 0 deletions tests/python/relay/test_ir_nodes.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -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 *
Expand Down Expand Up @@ -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])
Expand Down
25 changes: 24 additions & 1 deletion tests/python/relay/test_pass_alpha_equal.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
import tvm
from tvm import relay
from tvm.relay import analysis
from tvm.relay.testing import run_opt_pass

def alpha_equal(x, y):
"""
Expand Down Expand Up @@ -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))
Expand Down Expand Up @@ -607,6 +608,7 @@ 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
Expand Down Expand Up @@ -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()
Expand All @@ -672,3 +694,4 @@ def test_tuple_match():
test_var_alpha_equal()
test_graph_equal()
test_hash_unequal()
test_fn_attribute()
36 changes: 35 additions & 1 deletion tests/python/relay/test_pass_fuse_ops.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -35,6 +35,7 @@ def expected():
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)
Expand Down Expand Up @@ -76,6 +77,8 @@ def expected(dshape):
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")
Expand All @@ -86,6 +89,8 @@ def expected(dshape):
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")
Expand All @@ -94,6 +99,8 @@ def expected(dshape):
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")
Expand All @@ -104,6 +111,8 @@ def expected(dshape):
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])
Expand Down Expand Up @@ -135,13 +144,15 @@ def expected(dshape):
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)
upsampled = relay.nn.upsampling(p0, scale_h=2, scale_w=2, layout="NCHW")
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])
Expand Down Expand Up @@ -172,10 +183,12 @@ def expected(dshape):
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])
Expand Down Expand Up @@ -205,10 +218,12 @@ def expected(dshape):
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])
Expand Down Expand Up @@ -242,6 +257,7 @@ def expected(dshape, dtype):
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():
Expand Down Expand Up @@ -271,11 +287,13 @@ def expected(dim):
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))
Expand Down Expand Up @@ -306,11 +324,13 @@ def expected(dim):
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))
Expand Down Expand Up @@ -346,8 +366,9 @@ def before(x):

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)
Expand Down Expand Up @@ -388,15 +409,18 @@ def expected(dshape):
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])
Expand Down Expand Up @@ -438,30 +462,36 @@ def expected(dshape):
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")])
Expand Down Expand Up @@ -499,6 +529,7 @@ def expected():
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)
Expand Down Expand Up @@ -529,6 +560,7 @@ def expected():
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()
Expand Down Expand Up @@ -570,13 +602,15 @@ def expected():
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))
yy = xx
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)

Expand Down
Loading

0 comments on commit 95de08b

Please sign in to comment.