def test_ad():
shape = (10, 10)
dtype = "float32"
t = TensorType(shape, dtype)
d = Var("d", t)
f = Function([d], d * d)
g = dcpe(f, grad=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())
assert_alpha_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 _collapse_sum(children, attrs, odtype='float32'):
for key in ["axis", "keepdims", "exclude"]:
if key in attrs.attrs:
raise NotImplementedError("Parameter '" + key +
"' is not supported.")
return op.collapse_sum_like(children[0], children[1])
