def test_tuple():
shape = (10, 10)
dtype = 'float32'
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
y = relay.var("y", t)
z = relay.var("z", t)
tup = relay.Var("tup")
func = relay.Function(
[x, y, z],
relay.Let(
tup, relay.Tuple([x, y, z]),
relay.TupleGetItem(tup, 0) + relay.TupleGetItem(tup, 1) -
relay.TupleGetItem(tup, 2)))
back_func = relay.ir_pass.infer_type(gradient(func))
assert back_func.checked_type == relay.FuncType(
[t, t, t], relay.TupleType([t, relay.TupleType([t, t, t])]))
x_nd = rand(dtype, *shape)
y_nd = rand(dtype, *shape)
z_nd = rand(dtype, *shape)
x_np = x_nd.asnumpy()
y_np = y_nd.asnumpy()
z_np = z_nd.asnumpy()
expected_forward = x_np + y_np - z_np
ex = create_executor()
forward, (grad_x, grad_y, grad_z) = ex.evaluate(back_func)(x_nd, y_nd,
z_nd)
tvm.testing.assert_allclose(forward.asnumpy(), expected_forward)
tvm.testing.assert_allclose(grad_x.asnumpy(),
np.ones_like(grad_x.asnumpy()))
tvm.testing.assert_allclose(grad_y.asnumpy(),
np.ones_like(grad_y.asnumpy()))
tvm.testing.assert_allclose(grad_z.asnumpy(),
-1 * np.ones_like(grad_z.asnumpy()))
def test_broadcast_add():
shape1 = (3, 4, 1)
shape2 = (1, 5)
dtype = 'float32'
x_nd = rand(dtype, *shape1)
y_nd = rand(dtype, *shape2)
x_np = x_nd.asnumpy()
y_np = y_nd.asnumpy()
expected_forward = x_np + y_np
t1 = relay.TensorType(shape1, dtype)
t2 = relay.TensorType(shape2, dtype)
x = relay.var("x", t1)
y = relay.var("y", t2)
func = relay.Function([x, y], x + y)
full_func = relay.ir_pass.infer_type(gradient(func))
assert full_func.checked_type == relay.FuncType([t1, t2],
relay.TupleType([relay.TensorType(expected_forward.shape, dtype),
relay.TupleType([t1, t2])]))
ex = create_executor()
forward, (grad_x, grad_y) = ex.evaluate(full_func)(x_nd, y_nd)
tvm.testing.assert_allclose(forward.asnumpy(), expected_forward)
tvm.testing.assert_allclose(grad_x.asnumpy(),
np.ones_like(expected_forward).sum(axis=2, keepdims=True))
tvm.testing.assert_allclose(grad_y.asnumpy(),
np.ones_like(expected_forward).sum(axis=(0, 1), keepdims=True).squeeze(axis=0))
def test_broadcast_add():
shape1 = (3, 4, 1)
shape2 = (1, 5)
dtype = 'float32'
x_nd = rand(dtype, *shape1)
y_nd = rand(dtype, *shape2)
x_np = x_nd.asnumpy()
y_np = y_nd.asnumpy()
expected_forward = x_np + y_np
t1 = relay.TensorType(shape1, dtype)
t2 = relay.TensorType(shape2, dtype)
x = relay.var("x", t1)
y = relay.var("y", t2)
func = relay.Function([x, y], x + y)
full_func = relay.ir_pass.infer_type(gradient(func))
assert full_func.checked_type == relay.FuncType(
[t1, t2],
relay.TupleType([
relay.TensorType(expected_forward.shape, dtype),
relay.TupleType([t1, t2])
]))
ex = create_executor()
forward, (grad_x, grad_y) = ex.evaluate(full_func)(x_nd, y_nd)
tvm.testing.assert_allclose(forward.asnumpy(), expected_forward)
tvm.testing.assert_allclose(
grad_x.asnumpy(),
np.ones_like(expected_forward).sum(axis=2, keepdims=True))
tvm.testing.assert_allclose(
grad_y.asnumpy(),
np.ones_like(expected_forward).sum(axis=(0, 1),
keepdims=True).squeeze(axis=0))
def test_tuple():
shape = (10, 10)
dtype = 'float32'
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
y = relay.var("y", t)
z = relay.var("z", t)
tup = relay.Var("tup")
func = relay.Function([x, y, z], relay.Let(tup, relay.Tuple([x, y, z]),
relay.TupleGetItem(tup, 0) +
relay.TupleGetItem(tup, 1) -
relay.TupleGetItem(tup, 2)))
back_func = relay.ir_pass.infer_type(gradient(func))
assert back_func.checked_type == relay.FuncType([t, t, t], relay.TupleType([t, relay.TupleType([t, t, t])]))
x_nd = rand(dtype, *shape)
y_nd = rand(dtype, *shape)
z_nd = rand(dtype, *shape)
x_np = x_nd.asnumpy()
y_np = y_nd.asnumpy()
z_np = z_nd.asnumpy()
expected_forward = x_np + y_np - z_np
ex = create_executor()
forward, (grad_x, grad_y, grad_z) = ex.evaluate(back_func)(x_nd, y_nd, z_nd)
tvm.testing.assert_allclose(forward.asnumpy(), expected_forward)
tvm.testing.assert_allclose(grad_x.asnumpy(), np.ones_like(grad_x.asnumpy()))
tvm.testing.assert_allclose(grad_y.asnumpy(), np.ones_like(grad_y.asnumpy()))
tvm.testing.assert_allclose(grad_z.asnumpy(), -1 * np.ones_like(grad_z.asnumpy()))
def test_square_second_order():
shape = (10, 10)
dtype = 'float32'
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
func = relay.Function([x], x * x)
back_func = relay.ir_pass.infer_type(gradient(func))
y = relay.var("y", t)
back_func_adjusted = relay.Function([y], relay.TupleGetItem(relay.TupleGetItem(back_func(y), 1), 0))
back_func_adjusted = relay.ir_pass.infer_type(back_func_adjusted)
back_back_func = relay.ir_pass.infer_type(gradient(back_func_adjusted))
assert back_func.checked_type == relay.FuncType([t], relay.TupleType([t, relay.TupleType([t])]))
x_nd = rand(dtype, *shape)
ex = create_executor()
forward, (grad_x,) = ex.evaluate(back_back_func)(x_nd)
tvm.testing.assert_allclose(forward.asnumpy(), 2 * x_nd.asnumpy())
tvm.testing.assert_allclose(grad_x.asnumpy(), 2 * np.ones_like(grad_x.asnumpy()))
def test_square_second_order():
shape = (10, 10)
dtype = 'float32'
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
func = relay.Function([x], x * x)
back_func = relay.ir_pass.infer_type(gradient(func))
y = relay.var("y", t)
back_func_adjusted = relay.Function([y], relay.TupleGetItem(relay.TupleGetItem(back_func(y), 1), 0))
back_func_adjusted = relay.ir_pass.infer_type(back_func_adjusted)
back_back_func = relay.ir_pass.infer_type(gradient(back_func_adjusted))
assert back_func.checked_type == relay.FuncType([t], relay.TupleType([t, relay.TupleType([t])]))
x_nd = rand(dtype, *shape)
ex = create_executor()
forward, (grad_x,) = ex.evaluate(back_back_func)(x_nd)
tvm.testing.assert_allclose(forward.asnumpy(), 2 * x_nd.asnumpy())
tvm.testing.assert_allclose(grad_x.asnumpy(), 2 * np.ones_like(grad_x.asnumpy()))
def test_empty_ad():
shape = (10, 10)
dtype = "float32"
t = TensorType(shape, dtype)
d = Var("d", t)
f = Function([d], d)
g = dcpe(gradient(f))
expected = Function([d], Tuple([d, Tuple([op.ones_like(d)])]))
assert alpha_equal(g, expected)
def test_ad():
shape = (10, 10)
dtype = "float32"
t = relay.TensorType(shape, dtype)
d = relay.Var("d", t)
f = relay.Function([d], d * d)
g = dcpe(gradient(f))
m = d * d
o = relay.op.ones_like(m)
grad = relay.op.zeros_like(d) + relay.op.collapse_sum_like(
o * d, d) + relay.op.collapse_sum_like(o * d, d)
expected = relay.Function([d], relay.Tuple([m, relay.Tuple([grad])]))
assert alpha_equal(g, expected)
def test_ad():
# TODO(MK): fix me
shape = (10, 10)
dtype = "float32"
t = relay.TensorType(shape, dtype)
d = relay.Var("d", t)
f = relay.Function([d], d * d)
g = dcpe(gradient(f))
m = d * d
o = relay.op.ones_like(m)
grad = relay.op.zeros_like(d) + relay.op.collapse_sum_like(o * d, d) + relay.op.collapse_sum_like(o * d, d)
expected = relay.Function([d], relay.Tuple([m, relay.Tuple([grad])]))
assert alpha_equal(g, expected)
def test_sub():
shape = (10, 10)
dtype = 'float32'
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
func = relay.Function([x], x - x)
back_func = relay.ir_pass.infer_type(gradient(func))
assert back_func.checked_type == relay.FuncType([t], relay.TupleType([t, relay.TupleType([t])]))
ex = create_executor()
x = rand(dtype, *shape)
forward, (grad,) = ex.evaluate(back_func)(x)
tvm.testing.assert_allclose(forward.asnumpy(), np.zeros_like(x.asnumpy()))
tvm.testing.assert_allclose(grad.asnumpy(), np.zeros_like(x.asnumpy()))
def test_ad():
shape = (10, 10)
dtype = 'float32'
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
func = relay.Function([x], x + x)
back_func = relay.ir_pass.infer_type(gradient(func))
feats = detect_feature(back_func)
assert feats == set([
Feature.fVar, Feature.fTuple, Feature.fTupleGetItem, Feature.fFunction,
Feature.fOp, Feature.fCall, Feature.fLet, Feature.fRefCreate,
Feature.fRefRead, Feature.fRefWrite
])
def test_sub():
shape = (10, 10)
dtype = 'float32'
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
func = relay.Function([x], x - x)
back_func = relay.ir_pass.infer_type(gradient(func))
assert back_func.checked_type == relay.FuncType([t], relay.TupleType([t, relay.TupleType([t])]))
ex = create_executor()
x = rand(dtype, *shape)
forward, (grad,) = ex.evaluate(back_func)(x)
tvm.testing.assert_allclose(forward.asnumpy(), np.zeros_like(x.asnumpy()))
tvm.testing.assert_allclose(grad.asnumpy(), np.zeros_like(x.asnumpy()))
def dcpe(expr, mod=None, grad=False):
passes = [
transform.PartialEvaluate(),
transform.DeadCodeElimination(inline_once=True)
]
if grad:
expr = gradient(expr)
if mod:
assert isinstance(expr, Function)
mod[mod.entry_func] = expr
seq = transform.Sequential(passes)
mod = seq(mod)
return mod[mod.entry_func]
return transform.OptimizeOnExpr(expr, passes)
def test_pow():
mod = relay.Module()
p = Prelude(mod)
shape = (10, 10)
dtype = 'float32'
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
double = relay.Function([x], x + x)
i = relay.var("i", t)
func = relay.Function([i], relay.Call(p.iterate(double, p.s(p.s(p.s(p.z())))), [i]))
back_func = relay.ir_pass.infer_type(gradient(func, mod=mod), mod=mod)
assert back_func.checked_type == relay.FuncType([t], relay.TupleType([t, relay.TupleType([t])]))
i_nd = rand(dtype, *shape)
ex = create_executor(mod=mod)
forward, (grad_i,) = ex.evaluate(back_func)(i_nd)
tvm.testing.assert_allclose(forward.asnumpy(), 8 * i_nd.asnumpy())
tvm.testing.assert_allclose(grad_i.asnumpy(), 8 * np.ones_like(grad_i.asnumpy()))
def test_pow():
mod = relay.Module()
p = Prelude(mod)
shape = (10, 10)
dtype = 'float32'
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
double = relay.Function([x], x + x)
i = relay.var("i", t)
func = relay.Function([i], relay.Call(p.iterate(double, p.s(p.s(p.s(p.z())))), [i]))
back_func = relay.ir_pass.infer_type(gradient(func, mod=mod), mod=mod)
assert back_func.checked_type == relay.FuncType([t], relay.TupleType([t, relay.TupleType([t])]))
i_nd = rand(dtype, *shape)
ex = create_executor(mod=mod)
forward, (grad_i,) = ex.evaluate(back_func)(i_nd)
tvm.testing.assert_allclose(forward.asnumpy(), 8 * i_nd.asnumpy())
tvm.testing.assert_allclose(grad_i.asnumpy(), 8 * np.ones_like(grad_i.asnumpy()))
def check_single_op(opfunc, ref):
shape = (10, 4)
dtype = 'float32'
tp = relay.TensorType(shape, dtype)
x = relay.var("x", tp)
y = opfunc(x)
if ref is not None:
data = np.random.rand(*shape).astype(dtype)
ref_grad = ref(data)
fwd_func = relay.Function([x], y)
bwd_func = infer_type(gradient(fwd_func))
for target, ctx in ctx_list():
intrp = relay.create_executor(ctx=ctx, target=target)
op_res, (op_grad, ) = intrp.evaluate(bwd_func)(data)
np.testing.assert_allclose(op_grad.asnumpy(), ref_grad, rtol=0.01)
def test_ad():
shape = (10, 10)
dtype = "float32"
t = TensorType(shape, dtype)
d = Var("d", t)
f = Function([d], d * d)
g = dcpe(gradient(f))
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))
assert alpha_equal(g, expected)
def check_binary_op(opfunc, ref):
s = (5, 10, 5)
t = relay.TensorType((5, 10, 5))
x = relay.var("x", t)
y = relay.var("y", t)
z = opfunc(x, y)
x_data = np.random.rand(*s).astype(t.dtype)
y_data = np.random.rand(*s).astype(t.dtype)
ref_grad0, ref_grad1 = ref(x_data, y_data)
fwd_func = relay.Function([x, y], z)
bwd_func = infer_type(gradient(fwd_func))
for target, ctx in ctx_list():
intrp = relay.create_executor(ctx=ctx, target=target)
op_res, (op_grad0, op_grad1) = intrp.evaluate(bwd_func)(x_data, y_data)
np.testing.assert_allclose(op_grad0.asnumpy(), ref_grad0, rtol=0.01)
np.testing.assert_allclose(op_grad1.asnumpy(), ref_grad1, rtol=0.01)
def test_ref():
shape = (10, 10)
dtype = 'float32'
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
r = relay.Var("r")
u = relay.Var("u")
body = relay.RefRead(r)
body = relay.Let(u, relay.RefWrite(r, relay.RefRead(r) + relay.RefRead(r)), body)
body = relay.Let(r, relay.RefCreate(x), body)
func = relay.Function([x], body)
back_func = relay.ir_pass.infer_type(gradient(func))
assert back_func.checked_type == relay.FuncType([t], relay.TupleType([t, relay.TupleType([t])]))
x_nd = rand(dtype, *shape)
ex = create_executor()
forward, (grad_x,) = ex.evaluate(back_func)(x_nd)
tvm.testing.assert_allclose(forward.asnumpy(), 2 * x_nd.asnumpy())
tvm.testing.assert_allclose(grad_x.asnumpy(), 2 * np.ones_like(grad_x.asnumpy()))
def test_ref():
shape = (10, 10)
dtype = 'float32'
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
r = relay.Var("r")
u = relay.Var("u")
body = relay.RefRead(r)
body = relay.Let(u, relay.RefWrite(r, relay.RefRead(r) + relay.RefRead(r)), body)
body = relay.Let(r, relay.RefCreate(x), body)
func = relay.Function([x], body)
back_func = relay.ir_pass.infer_type(gradient(func))
assert back_func.checked_type == relay.FuncType([t], relay.TupleType([t, relay.TupleType([t])]))
x_nd = rand(dtype, *shape)
ex = create_executor()
forward, (grad_x,) = ex.evaluate(back_func)(x_nd)
tvm.testing.assert_allclose(forward.asnumpy(), 2 * x_nd.asnumpy())
tvm.testing.assert_allclose(grad_x.asnumpy(), 2 * np.ones_like(grad_x.asnumpy()))
def check_single_op(opfunc, ref):
shape = (10, 4)
dtype = 'float32'
tp = relay.TensorType(shape, dtype)
x = relay.var("x", tp)
y = opfunc(x)
if ref is not None:
data = np.random.rand(*shape).astype(dtype)
ref_grad = ref(data)
fwd_func = relay.Function([x], y)
bwd_func = infer_type(gradient(fwd_func))
for target, ctx in ctx_list():
intrp = relay.create_executor(ctx=ctx, target=target)
op_res, (op_grad, ) = intrp.evaluate(bwd_func)(data)
np.testing.assert_allclose(op_grad.asnumpy(),
ref_grad,
rtol=0.01)
def check_binary_op(opfunc, ref):
s = (5, 10, 5)
t = relay.TensorType((5, 10, 5))
x = relay.var("x", t)
y = relay.var("y", t)
z = opfunc(x, y)
x_data = np.random.rand(*s).astype(t.dtype)
y_data = np.random.rand(*s).astype(t.dtype)
ref_grad0, ref_grad1 = ref(x_data, y_data)
fwd_func = relay.Function([x, y], z)
bwd_func = infer_type(gradient(fwd_func))
for target, ctx in ctx_list():
intrp = relay.create_executor(ctx=ctx, target=target)
op_res, (op_grad0, op_grad1) = intrp.evaluate(bwd_func)(x_data,
y_data)
np.testing.assert_allclose(op_grad0.asnumpy(),
ref_grad0,
rtol=0.01)
np.testing.assert_allclose(op_grad1.asnumpy(),
ref_grad1,
rtol=0.01)
