def test_eq_ops():
a = tir.IntImm("int8", 1)
with pytest.raises(ValueError):
assert a != None
with pytest.raises(ValueError):
assert not a == None
b = tir.StringImm("abc")
assert b != None
assert not b == None
def expected():
data = relay.var('data', shape=(1, 512, 28, 28))
kernel = relay.var('kernel', shape=(256, 512, 1, 1))
bias = relay.var('bias', shape=(256, ))
a = relay.var('a', shape=(1, 256, 28, 28))
b = relay.var('b', shape=(1, 256, 28, 28))
# conv_bias_relu function
in_1 = relay.var('in_1', shape=(1, 512, 28, 28))
in_2 = relay.var('in_2', shape=(256, 512, 1, 1))
in_3 = relay.var('in_3', shape=(256, ))
conv_node = relay.nn.conv2d(in_1,
in_2,
kernel_size=(1, 1),
padding=(0, 0),
strides=(1, 1))
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))
in_5 = relay.var('in_5', shape=(1, 256, 28, 28))
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])
add_relu_1 = relay.Call(add_relu, [conv_bias_add_relu_1, a])
r = relay.multiply(add_relu_1, b)
return relay.Function([data, kernel, bias, a, b], r)
def tir_imm(obj, dtype=None) -> tir.PrimExpr:
if isinstance(obj, tir.PrimExpr):
return obj
if isinstance(obj, bool):
return tir.IntImm(dtype=dtype or 'bool', value=obj)
if isinstance(obj, float):
return tir.FloatImm(dtype=dtype or 'float32', value=obj)
if isinstance(obj, int):
return tir.IntImm(dtype=dtype or 'int32', value=obj)
if isinstance(obj, str):
return tir.StringImm(obj)
assert False
def after_B():
inputs = [relay.var('input_' + str(i), shape=(10, 10)) for i in range(8)]
add_relu_calls = []
for i in range(4):
x = relay.var('x' + str(i))
y = relay.var('x' + str(i))
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('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)
add = relay.add(add_relu_calls[0], add_relu_calls[1])
sub = relay.subtract(add_relu_calls[2], add_relu_calls[3])
out = relay.multiply(add, sub)
return relay.Function(inputs, out)
