tests/python/relay/test_pass_partial_eval.py

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import numpy as np
import tvm
import tvm.testing
from tvm import relay
from tvm.relay.prelude import Prelude
from tvm.relay import op, create_executor, transform
from tvm.relay import Var, TypeVar, TupleGetItem, Let, Function, const, RefRead, RefWrite, RefCreate
from tvm.relay import TensorType, Tuple, If, Clause, PatternConstructor, PatternVar, Match
from tvm.relay import GlobalVar, Call
from tvm.relay.transform import gradient
from tvm.relay.testing import make_nat_expr, run_infer_type


def check_eval(expr, expected_result, mod=None, rtol=1e-07):
    dev = tvm.device("llvm", 0)
    result = create_executor(mod=mod, device=dev, target="llvm").evaluate(expr)
    np.testing.assert_allclose(result.numpy(), expected_result, rtol=rtol)


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 tipe(expr):
    return run_opt_pass(expr, [transform.PartialEvaluate(), transform.InferType()])


def dcpe(expr, mod=None, grad=False, ignore_impurity=False):
    passes = [
        transform.PartialEvaluate(),
        transform.InferType(),
        transform.DeadCodeElimination(inline_once=True, ignore_impurity=ignore_impurity),
        transform.InferType(),
    ]
    if grad:
        expr = gradient(run_infer_type(expr))
    if mod:
        assert isinstance(expr, Function)
        mod["main"] = expr
        seq = tvm.transform.Sequential(passes)
        mod = seq(mod)
        return mod["main"]
    return run_opt_pass(expr, passes)


def test_tuple():
    t = TypeVar("t")
    x = Var("x", t)
    body = TupleGetItem(relay.Tuple([relay.const(4.0), x]), 1)
    f = Function([x], body, None, [t])
    expected = relay.Function([x], x, None, [t])
    expected = run_opt_pass(expected, transform.InferType())
    assert tvm.ir.structural_equal(dcpe(f), expected)


def test_const_inline():
    t = relay.TensorType([], "float32")
    d = Var("d", t)
    double = Function([d], d + d)
    orig = double(const(4.0))
    assert tvm.ir.structural_equal(dcpe(orig), const(8.0))


def test_ref():
    t = relay.TensorType([], "float32")
    d = relay.Var("d", t)
    r = relay.Var("r", relay.RefType(t))
    x = relay.Var("x")
    body = relay.RefRead(r)
    body = Let(x, RefWrite(r, RefRead(r) * RefRead(r)), body)
    body = Let(r, RefCreate(d), body)
    square = Function([d], body)
    expected = run_opt_pass(Function([d], d * d), transform.InferType())
    # TODO(mbs): Revisit once DCE eliminates dead writes.
    actual = dcpe(square, ignore_impurity=True)
    assert tvm.ir.structural_equal(actual, expected)


def test_empty_ad():
    shape = (10, 10)
    dtype = "float32"
    t = TensorType(shape, dtype)
    d = Var("d", t)
    f = Function([d], d)
    # TODO(mbs): Revisit once DCE eliminates dead writes.
    g = dcpe(f, grad=True, ignore_impurity=True)
    expected = Function([d], Tuple([d, Tuple([op.ones_like(d)])]))
    expected = run_opt_pass(expected, transform.InferType())
    assert tvm.ir.structural_equal(g, expected)


def test_ad():
    shape = (10, 10)
    dtype = "float32"
    t = TensorType(shape, dtype)
    d = Var("d", t)
    f = Function([d], d * d)
    # TODO(mbs): Revisit once DCE eliminates dead writes.
    g = dcpe(f, grad=True, ignore_impurity=True)
    m = d * d
    x = relay.Var("x")
    o = op.ones_like(x)
    x1 = relay.Var("x1")
    grad = op.zeros_like(d) + op.collapse_sum_like(x1 * d, d) + op.collapse_sum_like(x1 * d, d)
    body = Tuple([x, Tuple([grad])])
    body = relay.Let(x1, o, body)
    expected = Function([d], relay.Let(x, m, body))
    expected = run_opt_pass(expected, transform.InferType())
    tvm.ir.assert_structural_equal(g, expected)


def test_if_ref():
    shape = ()
    dtype = "bool"
    t = TensorType(shape, dtype)
    d = Var("d", t)
    r = Var("r")
    update = Function([], RefWrite(r, RefRead(r) + RefRead(r)))
    u = Var("u")
    body = If(d, u(), u())
    eff = Var("eff")
    body = Let(eff, body, RefRead(r))
    f = Function([d], Let(r, RefCreate(const(1)), Let(u, update, body)))
    pe_f = tipe(f)
    f_res = create_executor().evaluate(f)(const(True))
    pe_f_res = create_executor().evaluate(pe_f)(const(True))
    np.testing.assert_allclose(f_res.numpy(), 2 * np.ones_like(f_res.numpy()))
    np.testing.assert_allclose(pe_f_res.numpy(), 2 * np.ones_like(pe_f_res.numpy()))


def test_function_invalidate():
    shape = ()
    dtype = "bool"
    t = TensorType(shape, dtype)
    d = Var("d", t)
    r = Var("r")
    fetch = Function([], RefRead(r))
    fet = Var("fetch")
    fet_obscured = Var("fetch_obscured")
    u = Var("u")
    body = If(d, fet_obscured(), fet_obscured())
    body = Let(u, RefWrite(r, const(1)), body)
    body = Let(fet_obscured, If(d, fet, fet), body)
    body = Let(fet, fetch, body)
    body = Let(r, RefCreate(const(0)), body)
    f = Function([d], body)
    pe_f = tipe(f)
    f_res = create_executor().evaluate(f)(const(True))
    pe_f_res = create_executor().evaluate(pe_f)(const(True))
    np.testing.assert_allclose(f_res.numpy(), np.ones_like(f_res.numpy()))
    np.testing.assert_allclose(pe_f_res.numpy(), np.ones_like(pe_f_res.numpy()))


def test_head_cons():
    mod = tvm.IRModule()
    p = Prelude(mod)
    t = TypeVar("t")
    x = Var("x", t)
    rlist, cons, nil = p.mod.get_type("List")
    hd = p.mod.get_global_var("hd")
    body = hd(cons(x, nil()))
    f = Function([x], body, None, [t])
    res = dcpe(f, mod)
    expected_mod = tvm.IRModule.from_expr(Function([x], x, t, [t]))
    assert tvm.ir.structural_equal(res, expected_mod["main"])


def test_map():
    mod = tvm.IRModule()
    p = Prelude(mod)
    rlist, cons, nil = p.mod.get_type("List")
    rmap = p.mod.get_global_var("map")
    f = GlobalVar("f")
    t = TypeVar("t")
    a = Var("a", t)
    mod[f] = Function([a], a, t, [t])
    orig = rmap(f, cons(const(1), cons(const(2), cons(const(3), nil()))))
    expected = cons((const(1)), cons((const(2)), cons((const(3)), nil())))
    expected = Function([], expected)
    mod["main"] = expected
    mod = transform.InferType()(mod)
    expected = mod["main"]
    orig = Function([], orig)
    res = dcpe(orig, mod=mod)
    assert tvm.ir.structural_equal(res.body, expected.body)


def test_loop():
    mod = tvm.IRModule()
    t = TypeVar("t")
    x = Var("x", t)
    loop = GlobalVar("loop")
    mod[loop] = Function([x], loop(x), t, [t])
    expected = Call(loop, [const(1)])
    mod["main"] = Function([], expected)
    mod = transform.InferType()(mod)
    expected = mod["main"].body
    call = Function([], loop(const(1)))
    res = dcpe(call, mod=mod)
    assert tvm.ir.structural_equal(res.body, expected)


def test_swap_loop():
    mod = tvm.IRModule()
    p = Prelude(mod)
    p.mod.import_from_std("nat.rly")
    nat, _, _ = p.mod.get_type("nat")
    x = Var("x", nat())
    y = Var("y", nat())
    loop = GlobalVar("loop")
    mod[loop] = Function([x, y], loop(y, x), nat())
    prog = loop(make_nat_expr(p, 1), make_nat_expr(p, 2))
    res = Function([], prog)
    res = dcpe(res, mod=mod)
    assert tvm.ir.structural_equal(prog, res.body)


def test_abs_diff():
    # TODO(@M.K.): refactor using tuple pattern (not yet implemented)
    mod = tvm.IRModule()
    p = Prelude(mod)
    p.mod.import_from_std("nat.rly")
    nat, z, s = p.mod.get_type("nat")
    x = Var("x", nat())
    y = Var("y", nat())
    xp = Var("x'", nat())
    yp = Var("y'", nat())
    diff = GlobalVar("diff")
    y_z_case = Clause(PatternConstructor(z, []), x)
    y_s_case = Clause(PatternConstructor(s, [PatternVar(yp)]), diff(yp, xp))
    x_z_case = Clause(PatternConstructor(z, []), y)
    x_s_case = Clause(PatternConstructor(s, [PatternVar(xp)]), Match(y, [y_z_case, y_s_case]))
    mod[diff] = Function([x, y], Match(x, [x_z_case, x_s_case]))
    orig = diff(make_nat_expr(p, 7), make_nat_expr(p, 3))
    orig = Function([], orig)
    res = dcpe(orig, mod=mod)
    assert tvm.ir.structural_equal(res.body, make_nat_expr(p, 4))


def test_match_nat_id():
    mod = tvm.IRModule()
    p = Prelude(mod)
    p.mod.import_from_std("nat.rly")
    nat, z, s = p.mod.get_type("nat")
    x = Var("x", nat())
    y = Var("y", nat())
    nat_id = GlobalVar("nat_id")
    z_case = Clause(PatternConstructor(z, []), z())
    s_case = Clause(PatternConstructor(s, [PatternVar(y)]), s(y))
    mod[nat_id] = Function([x], Match(x, [z_case, s_case]))
    orig = nat_id(make_nat_expr(p, 3))
    orig = Function([], orig)
    res = dcpe(orig, mod=mod)
    assert tvm.ir.structural_equal(res.body, make_nat_expr(p, 3))


def test_nat_id():
    mod = tvm.IRModule()
    p = Prelude(mod)
    p.mod.import_from_std("nat.rly")
    nat, _, _ = p.mod.get_type("nat")
    x = Var("x", nat())
    y = Var("y", nat())
    nat_id = GlobalVar("nat_id")
    mod[nat_id] = Function([x], x)
    orig = nat_id(make_nat_expr(p, 3))
    orig = Function([], orig)
    res = dcpe(orig, mod=mod)
    assert tvm.ir.structural_equal(res.body, make_nat_expr(p, 3))


def test_global_match_nat_id():
    mod = tvm.IRModule()
    p = Prelude(mod)
    p.mod.import_from_std("nat.rly")
    nat, z, s = p.mod.get_type("nat")
    x = Var("x", nat())
    z_case = Clause(PatternConstructor(z, []), z())
    s_case = Clause(PatternConstructor(s, [PatternVar(x)]), s(x))
    orig = Match(make_nat_expr(p, 3), [z_case, s_case])
    orig = Function([], orig)
    res = dcpe(orig, mod=mod)
    assert tvm.ir.structural_equal(res.body, make_nat_expr(p, 3))


def test_double():
    mod = tvm.IRModule()
    p = Prelude(mod)
    p.mod.import_from_std("nat.rly")
    double = p.mod.get_global_var("nat_double")
    orig = double(make_nat_expr(p, 3))
    orig = Function([], orig)
    res = dcpe(orig, mod=mod)
    assert tvm.ir.structural_equal(res.body, make_nat_expr(p, 6))


def test_concat():
    t = relay.TensorType([10], "float32")
    x = Var("x", t)
    y = Var("x", t)
    orig = run_infer_type(Function([x, y], op.concatenate([x, y], axis=0)))
    tvm.ir.assert_structural_equal(dcpe(orig), orig)


def test_triangle_number():
    t = relay.TensorType([], "int32")
    x = Var("x", t)
    f_var = Var("f")
    f = Function([x], If(op.equal(x, const(0)), const(0), x + f_var(x - const(1))))
    orig = run_infer_type(Let(f_var, f, f_var(const(10))))
    tvm.ir.assert_structural_equal(dcpe(orig), const(55))


def test_nat_update():
    m = tvm.IRModule()
    p = Prelude(m)
    p.mod.import_from_std("nat.rly")
    m = transform.ToANormalForm()(m)
    transform.PartialEvaluate()(m)


def test_tuple_match():
    a = relay.Var("a")
    b = relay.Var("b")
    clause = relay.Clause(relay.PatternTuple([relay.PatternVar(a), relay.PatternVar(b)]), a + b)
    x = relay.Match(relay.Tuple([relay.const(1), relay.const(1)]), [clause])
    tvm.ir.assert_structural_equal(dcpe(x), const(2))


if __name__ == "__main__":
    tvm.testing.main()