Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[autodiff] Support binary operators for forward mode #5389

Merged
merged 1 commit into from
Jul 11, 2022
Merged

[autodiff] Support binary operators for forward mode #5389

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
20 changes: 20 additions & 0 deletions taichi/transforms/auto_diff.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1172,6 +1172,26 @@ class MakeDual : public ADTransform {
accumulate(bin, div(dual(bin->lhs), bin->rhs));
accumulate(bin, negate(div(mul(dual(bin->rhs), bin->lhs),
mul(bin->rhs, bin->rhs))));
} else if (bin->op_type == BinaryOpType::atan2) {
auto numerator = add(sqr(bin->lhs), sqr(bin->rhs));
accumulate(bin, div(mul(bin->rhs, dual(bin->lhs)), numerator));
accumulate(bin, negate(div(mul(bin->lhs, dual(bin->rhs)), numerator)));
} else if (bin->op_type == BinaryOpType::pow) {
// d (x ^ y) = x ^ (y-1) * (y * dx + log(x) * x * dy)
auto common_coeff =
pow(bin->lhs, sub(bin->rhs, constant(1))); // x ^ (y-1)
accumulate(bin, mul(dual(bin->lhs), mul(bin->rhs, common_coeff)));
accumulate(bin, mul(dual(bin->rhs),
mul(log(bin->lhs), mul(bin->lhs, common_coeff))));
} else if (bin->op_type == BinaryOpType::min ||
bin->op_type == BinaryOpType::max) {
auto cmp = bin->op_type == BinaryOpType::min ? cmp_lt(bin->lhs, bin->rhs)
: cmp_lt(bin->rhs, bin->lhs);
auto zero = insert<ConstStmt>(TypedConstant(bin->ret_type));
accumulate(bin, sel(cmp, dual(bin->lhs), zero));
accumulate(bin, sel(cmp, zero, dual(bin->rhs)));
} else if (bin->op_type == BinaryOpType::floordiv) {
// do nothing
} else if (is_comparison(bin->op_type) || is_bit_op(bin->op_type)) {
// do nothing
} else {
Expand Down
41 changes: 36 additions & 5 deletions tests/python/test_ad_basics.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -159,9 +159,10 @@ def test_unary(tifunc, npfunc):
(lambda x: ti.max(1, x), lambda x: np.maximum(1, x)),
])
@if_has_autograd
@test_utils.test()
@test_utils.test(exclude=[ti.cc])
def test_minmax(tifunc, npfunc):
grad_test(tifunc, npfunc)
grad_test_fwd(tifunc, npfunc)


@if_has_autograd
Expand All @@ -188,44 +189,74 @@ def func2():
func2.grad()


@if_has_autograd
@test_utils.test()
def test_mod_fwd():
x = ti.field(ti.f32)
y = ti.field(ti.f32)

ti.root.dense(ti.i, 1).place(x, y)
ti.root.lazy_dual()

@ti.kernel
def func():
y[0] = x[0] % 3

@ti.kernel
def func2():
ti.atomic_add(y[0], x[0] % 3)

with ti.ad.FwdMode(loss=y, parameters=x, seed=[1.0]):
func()
func2()


@pytest.mark.parametrize('tifunc,npfunc', [
(lambda x: ti.atan2(0.4, x), lambda x: np.arctan2(0.4, x)),
(lambda y: ti.atan2(y, 0.4), lambda y: np.arctan2(y, 0.4)),
])
@if_has_autograd
@test_utils.test()
@test_utils.test(exclude=[ti.cc])
def test_atan2(tifunc, npfunc):
grad_test(tifunc, npfunc)
grad_test_fwd(tifunc, npfunc)


@pytest.mark.parametrize('tifunc,npfunc', [
(lambda x: ti.atan2(0.4, x), lambda x: np.arctan2(0.4, x)),
(lambda y: ti.atan2(y, 0.4), lambda y: np.arctan2(y, 0.4)),
])
@if_has_autograd
@test_utils.test(require=ti.extension.data64, default_fp=ti.f64)
@test_utils.test(require=ti.extension.data64,
default_fp=ti.f64,
exclude=[ti.cc])
def test_atan2_f64(tifunc, npfunc):
grad_test(tifunc, npfunc)
grad_test_fwd(tifunc, npfunc)


@pytest.mark.parametrize('tifunc,npfunc', [
(lambda x: 0.4**x, lambda x: np.power(0.4, x)),
(lambda y: y**0.4, lambda y: np.power(y, 0.4)),
])
@if_has_autograd
@test_utils.test()
@test_utils.test(exclude=[ti.cc])
def test_pow(tifunc, npfunc):
grad_test(tifunc, npfunc)
grad_test_fwd(tifunc, npfunc)


@pytest.mark.parametrize('tifunc,npfunc', [
(lambda x: 0.4**x, lambda x: np.power(0.4, x)),
(lambda y: y**0.4, lambda y: np.power(y, 0.4)),
])
@if_has_autograd
@test_utils.test(require=ti.extension.data64, default_fp=ti.f64)
@test_utils.test(require=ti.extension.data64,
default_fp=ti.f64,
exclude=[ti.cc])
def test_pow_f64(tifunc, npfunc):
grad_test(tifunc, npfunc)
grad_test_fwd(tifunc, npfunc)


@test_utils.test()
Expand Down