def test_multivar_reverse_ad():
"""Simple test with multivariate reverse mode ad."""
mod = tvm.IRModule()
shape = (10, 10)
dtype = "float32"
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
y = relay.var("y", t)
func = relay.Function([x, y], (x * y) * relay.const(np.ones(shape, dtype)))
func = run_infer_type(func)
back_func = transform.gradient(func)
back_func = run_infer_type(back_func)
mod["main"] = back_func
mod = transform.InferType()(mod)
mod = transform.LazyGradientInit()(mod)
back_func = mod["main"]
assert mod["main"].checked_type == relay.FuncType(
[t, t], relay.TupleType([t, relay.TupleType([t, t])])
)
x = rand(dtype, *shape)
y = rand(dtype, *shape)
(forward), (grad_x, grad_y,) = create_executor(mod=mod).evaluate(
back_func
)(x, y)
assert_allclose(forward.numpy(), x.numpy() * y.numpy())
assert_allclose(grad_x.numpy(), y.numpy())
assert_allclose(grad_y.numpy(), x.numpy())
def test_add_broadcast():
"""Test adding matrices of different size. Check types and semantic equivalence."""
mod = tvm.IRModule()
shape1 = (3, 4, 1)
shape2 = (1, 5)
dtype = "float32"
t1 = relay.TensorType(shape1, dtype)
t2 = relay.TensorType(shape2, dtype)
x1 = relay.var("x1", t1)
x2 = relay.var("x2", t2)
func = relay.Function([x1, x2], x1 + x2)
func = run_infer_type(func)
mod["main"] = func
mod = transform.InferType()(mod)
mod = transform.LazyGradientInit()(mod)
func = mod["main"]
x1_np = rand(dtype, *shape1).numpy()
x2_np = rand(dtype, *shape2).numpy()
expected_forward = x1_np + x2_np
expected_forward_type = relay.TensorType(expected_forward.shape, dtype)
assert mod["main"].checked_type == relay.FuncType([t1, t2], expected_forward_type)
forward = create_executor(mod=mod).evaluate(func)(x1_np, x2_np)
assert_allclose(forward.numpy(), expected_forward)
def test_reverse_ad_identity():
"""Simple test with reverse mode ad."""
# of f(x) = x
mod = tvm.IRModule()
shape = (10, 10)
dtype = "float32"
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
func = relay.Function([x], x)
func = run_infer_type(func)
back_func = transform.gradient(func)
back_func = run_infer_type(back_func)
mod["main"] = back_func
mod = transform.InferType()(mod)
mod = transform.LazyGradientInit()(mod)
back_func = mod["main"]
assert mod["main"].checked_type == relay.FuncType(
[t], relay.TupleType([t, relay.TupleType([t])])
)
x = rand(dtype, *shape)
(forward), (grad,) = create_executor(mod=mod).evaluate(back_func)(x)
assert_allclose(forward.numpy(), x.numpy())
assert_allclose(grad.numpy(), np.ones_like(x.numpy()))
def test_ret_tuple():
"""Test tuple return type. Check types and semantic equivalence."""
mod = tvm.IRModule()
shape = (10, 10)
dtype = "float32"
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
# f(x) = (x,x)
func = relay.Function([x], relay.Tuple([x, x * relay.const(2.0)]))
func = run_infer_type(func)
mod["main"] = func
mod = transform.InferType()(mod)
mod = transform.LazyGradientInit()(mod)
func = mod["main"]
assert mod["main"].checked_type == relay.FuncType([t],
relay.TupleType([t, t]))
x = rand(dtype, *shape)
y = create_executor(mod=mod).evaluate(func)(x)
assert_allclose(y[0].numpy(), x.numpy())
assert_allclose(y[1].numpy(), x.numpy() * 2.0)
def test_add_tuple():
"""Add elements of tuple. Check types and semantic equivalence."""
mod = tvm.IRModule()
shape = (10, 10)
dtype = "float32"
tensor_type = relay.TensorType(shape, dtype)
t = relay.TupleType([tensor_type, tensor_type])
x = relay.var("x", t)
# f((x1,x2)) = x1 + x2
y = relay.Function([x],
relay.TupleGetItem(x, 0) + relay.TupleGetItem(x, 1))
mod["main"] = y
mod = transform.InferType()(mod)
mod = transform.LazyGradientInit()(mod)
mod = tvm.transform.PrintIR(show_meta_data=True)(mod)
y = mod["main"]
assert mod["main"].checked_type == relay.FuncType([t], tensor_type)
x = (rand(dtype, *shape), rand(dtype, *shape))
y = create_executor(mod=mod).evaluate(y)(x)
assert_allclose(y.numpy(), x[0].numpy() + x[1].numpy())
def test_before_partial_eval():
"""Test transformation before PartialEval"""
mod = tvm.IRModule()
shape = (10, 10)
dtype = "float32"
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
y = relay.var("y", t)
func = relay.Function([x, y], x * y)
func = run_infer_type(func)
back_func = transform.gradient(func)
back_func = run_infer_type(back_func)
mod["main"] = back_func
seq = tvm.transform.Sequential(
[transform.LazyGradientInit(), transform.PartialEvaluate(), transform.DeadCodeElimination()]
)
mod = seq(mod)
back_func = mod["main"]
assert mod["main"].checked_type == relay.FuncType(
[t, t], relay.TupleType([t, relay.TupleType([t, t])])
)
ex = create_executor(mod=mod)
x = rand(dtype, *shape)
y = rand(dtype, *shape)
def test_reverse_ad_identity():
"""Simple test with reverse mode ad."""
# of f(x) = x
mod = tvm.IRModule()
shape = (10, 10)
dtype = "float32"
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
func = relay.Function([x], x)
func = run_infer_type(func)
back_func = transform.gradient(func)
back_func = run_infer_type(back_func)
mod["main"] = back_func
mod = transform.LazyGradientInit()(mod)
back_func = mod["main"]
assert mod["main"].checked_type == relay.FuncType(
[t], relay.TupleType([t, relay.TupleType([t])])
)
ex = create_executor(mod=mod)
x = rand(dtype, *shape)
def test_tc():
"""Simple testcase, check that transformation typechecks."""
mod = tvm.IRModule()
shape = (20, 20)
dtype = "float32"
t = relay.TensorType(shape, dtype)
x1 = relay.var("x1", t)
x2 = relay.var("x2", t)
# f(x1,x2) = (x1-x2)*x2
y = relay.Function([x1, x2], (x1 - x2) * x2)
mod["main"] = y
mod = transform.LazyGradientInit()(mod)
# function input/output types should remain the same
assert mod["main"].checked_type == relay.FuncType([t, t], t)
def test_after_partial_eval():
"""Test transformation following reverse mode ad and PartialEval"""
mod = tvm.IRModule()
shape = (10, 10)
dtype = "float32"
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
y = relay.var("y", t)
func = relay.Function([x, y], (x * y) * relay.const(np.ones(shape, dtype)))
func = run_infer_type(func)
back_func = transform.gradient(func)
back_func = run_infer_type(back_func)
mod["main"] = back_func
back_func = mod["main"]
seq = tvm.transform.Sequential(
[
transform.PartialEvaluate(),
transform.InferType(),
transform.LazyGradientInit(),
transform.InferType(),
transform.DeadCodeElimination(),
transform.InferType(),
]
)
mod = seq(mod)
assert mod["main"].checked_type == relay.FuncType(
[t, t], relay.TupleType([t, relay.TupleType([t, t])])
)
x = rand(dtype, *shape)
y = rand(dtype, *shape)
(forward), (grad_x, grad_y,) = create_executor(mod=mod).evaluate(
back_func
)(x, y)
assert_allclose(forward.numpy(), x.numpy() * y.numpy())
assert_allclose(grad_x.numpy(), y.numpy())
assert_allclose(grad_y.numpy(), x.numpy())
def test_ones_like():
"""Simple test using "ones_like" op"""
mod = tvm.IRModule()
shape = (10, 10)
dtype = "float32"
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
y = relay.Function([x], x + relay.ones_like(x))
mod["main"] = y
mod = transform.InferType()(mod)
mod = transform.LazyGradientInit()(mod)
y = mod["main"]
assert mod["main"].checked_type == relay.FuncType([t], t)
x = rand(dtype, *shape)
y = create_executor(mod=mod).evaluate(y)(x)
assert_allclose(y.numpy(), x.numpy() + np.ones_like(x.numpy()))
def test_zeros():
"""Simple test using "zeros" op"""
mod = tvm.IRModule()
shape = (10, 10)
dtype = "float32"
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
y = relay.Function([x], x + relay.zeros(shape, dtype))
mod["main"] = y
mod = transform.LazyGradientInit()(mod)
y = mod["main"]
assert mod["main"].checked_type == relay.FuncType([t], t)
ex = create_executor(mod=mod)
x = rand(dtype, *shape)
y = ex.evaluate(y)(x)
assert_allclose(y.asnumpy(), x.asnumpy())
def test_mult():
"""Simple multiplication testcase. Check types and semantic equivalence."""
mod = tvm.IRModule()
shape = (15, 15)
dtype = "float32"
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
# f(x) = x*x
y = relay.Function([x], x * x)
mod["main"] = y
mod = transform.InferType()(mod)
mod = transform.LazyGradientInit()(mod)
y = mod["main"]
assert mod["main"].checked_type == relay.FuncType([t], t)
x = rand(dtype, *shape)
y = create_executor(mod=mod).evaluate(y)(x)
assert_allclose(y.numpy(), x.numpy() * x.numpy())
def test_add():
"""Simple add testcase. Check types and semantic equivalence."""
mod = tvm.IRModule()
shape = (10, 10)
dtype = "float32"
t = relay.TensorType(shape, dtype)
x = relay.var("x", t)
# f(x) = x+x
y = relay.Function([x], x + x)
mod["main"] = y
mod = transform.LazyGradientInit()(mod)
y = mod["main"]
assert mod["main"].checked_type == relay.FuncType([t], t)
ex = create_executor(mod=mod)
x = rand(dtype, *shape)
y = ex.evaluate(y)(x)
assert_allclose(y.asnumpy(), x.asnumpy() + x.asnumpy())
