def test_recursive_concat():
"""
fn @concat_loop(%i: int32, %st: (any, 1)) -> (any, 1) {
if (%i < 10) {
let %i = reshape(cast(i, "float32"), newshape=(1, ))
let %new_st = concatenate((st, i), axis=0)
concat_loop(%i + 1, )
} else {
st
}
}
"""
# Initial Values.
i = relay.var("i", shape=(), dtype="int32")
st = relay.var("st", shape=(relay.Any(), 1), dtype="int32")
def _cond(i, st):
return relay.op.min(relay.op.less(i, int32(10)))
def _body(i, st):
i_vec = relay.op.reshape(i, (1, 1))
ret = relay.op.concatenate([st, i_vec], axis=0)
return i + int32(1), ret
loop = while_loop(_cond, [i, st], _body)
start = relay.var("start", shape=(), dtype="int32")
body = loop(start, relay.op.reshape(relay.const(0), newshape=(1, 1)))
func = relay.Function([start], relay.TupleGetItem(body, 1))
mod = tvm.IRModule()
mod["main"] = func
data = np.array(0.0, dtype="int32")
ref = np.array([0] + list(range(10))).reshape((11, 1)).astype("int32")
check_result([data], mod, ref)
def test_loop_free_var():
x = relay.var("x", shape=(), dtype="int32")
i = relay.var("i", shape=(), dtype="int32")
s = relay.var("s", shape=(), dtype="int32")
def cond(i, _):
return i < relay.const(10, dtype="int32")
def body_no_free_var(i, acc):
incr = relay.const(1, "int32")
return i + incr, acc + i
def body_with_free_var(i, acc):
incr = relay.const(1, "int32")
return i + incr, acc + x
for args, body, expected in zip([[], [1]],
[body_no_free_var, body_with_free_var],
[45, 10]):
loop = while_loop(cond, [i, s], body)
tup = loop(relay.const(0, dtype="int32"),
relay.zeros(shape=(), dtype="int32"))
ret = relay.TupleGetItem(tup, 1)
mod = tvm.IRModule()
mod["main"] = relay.Function(relay.analysis.free_vars(ret), ret)
check_result(args, expected, mod=mod)
def test_dynamic_concat():
"""
fn @concat_loop(%i: int32, %st: (any, 1)) -> (any, 1) {
if (%i < 10) {
let %i = reshape(cast(i, "float32"), newshape=(1, ))
let %new_st = concatenate((st, i), axis=0)
concat_loop(%i + 1, )
} else {
st
}
}
"""
# Initial Values.
i = relay.var('i', shape=(), dtype='int32')
st = relay.var('st', shape=(relay.Any(), 1), dtype='int32')
def _cond(i, st):
return relay.op.min(relay.op.less(i, int32(10)))
def _body(i, st):
i_vec = relay.op.reshape(i, (1, 1))
ret = relay.op.concatenate([st, i_vec], axis=0)
return i + int32(1), ret
loop = while_loop(_cond, [i, st], _body)
start = relay.var('start', shape=(), dtype='int32')
body = loop(start, relay.op.reshape(relay.const(0), newshape=(1, 1)))
func = relay.Function([start], relay.TupleGetItem(body, 1))
func = infer_type(func)
def test_recursive_concat_with_wrong_annotation():
"""
v0.0.1
fn (%start: int32) {
%7 = {
let %while_loop = fn (%i: int32, %st: Tensor[(1, 1), int32]) {
%0 = less(%i, 10)
%1 = min(%0)
if (%1) {
%2 = add(%i, 1)
%3 = reshape(%i, newshape=[1, 1])
%4 = (%st, %3)
/* The result of concat should be 1,1 but it is 2, 1. */
%5 = concatenate(%4)
%while_loop(%2, %5)
} else {
(%i, %st)
}
}
%6 = reshape(0, newshape=[1, 1])
%while_loop(%start, %6)
}
%7.1
}
"""
# Initial Values.
i = relay.var('i', shape=(), dtype='int32')
st = relay.var('st', shape=(1, 1), dtype='int32')
def _cond(i, st):
return relay.op.min(relay.op.less(i, int32(10)))
def _body(i, st):
i_vec = relay.op.reshape(i, (1, 1))
ret = relay.op.concatenate([st, i_vec], axis=0)
return i + int32(1), ret
loop = while_loop(_cond, [i, st], _body)
start = relay.var('start', shape=(), dtype='int32')
body = loop(start, relay.op.reshape(relay.const(0), newshape=(1, 1)))
func = relay.Function([start], relay.TupleGetItem(body, 1))
try:
func = infer_type(func)
assert False
except Exception as e:
assert "in particular dimension 0 conflicts 2 does not match 1" in str(
e)
def test_recursive_concat():
"""
fn @concat_loop(%i: int32, %st: (any, 1)) -> (any, 1) {
if (%i < 10) {
let %i = reshape(cast(i, "float32"), newshape=(1, ))
let %new_st = concatenate((st, i), axis=0)
concat_loop(%i + 1, )
} else {
st
}
}
"""
# Initial Values.
i = relay.var('i', shape=(), dtype='int32')
st = relay.var('st', shape=(relay.Any(), 1), dtype='int32')
def _cond(i, st):
return relay.op.min(relay.op.less(i, int32(10)))
def _body(i, st):
i_vec = relay.op.reshape(i, (1, 1))
ret = relay.op.concatenate([st, i_vec], axis=0)
return i + int32(1), ret
loop = while_loop(_cond, [i, st], _body)
start = relay.var('start', shape=(), dtype='int32')
body = loop(start, relay.op.reshape(relay.const(0), newshape=(1, 1)))
func = relay.Function([start], relay.TupleGetItem(body, 1))
mod = tvm.IRModule()
mod["main"] = func
data = np.array(0.0, dtype='int32')
ref = np.array([0] + list(range(10))).reshape((11, 1)).astype("int32")
# TODO(@jroesch): After LambdaLift pass, TypeInfer pass will fail
# so currently we cannot run this test case on VM
for kind in ["debug"]:
ex = relay.create_executor(kind, mod=mod, ctx=tvm.cpu(), target="llvm")
result = ex.evaluate()(data)
np.testing.assert_allclose(result.asnumpy(), ref)
