def run_test(inp_shp, fn):
np_x = np.random.randn(*inp_shp).astype(np.float32)
mge_x = megengine.tensor(np_x)
out_ref = fn(np_x)
if symbolic is not None:
fn = jit.trace(symbolic=symbolic)(fn)
for i in range(3):
out = fn(mge_x)
np.testing.assert_equal(out.numpy(), out_ref)
def test_sgd_momentum(monkeypatch, trace_mode, inplace_mode):
with monkeypatch.context() as mk:
mk.setenv("MEGENGINE_INPLACE_UPDATE", str(int(inplace_mode)))
def train_func(data, *, model=None, optim=None, gm=None):
optim.clear_grad()
with gm:
loss = net(data)
gm.backward(loss)
optim.step()
return loss
if trace_mode is not None:
train_func = trace(symbolic=trace_mode)(train_func)
def eval_func(data, *, model=None, optim=None, gm=None):
loss = net(data)
return loss
if trace_mode is not None:
eval_func = trace(symbolic=trace_mode)(eval_func)
net = Simple()
optim = optimizer.SGD(net.parameters(), lr=1.0, momentum=0.9)
gm = ad.GradManager().attach(net.parameters())
data = tensor([2.34])
train_func(data, model=net, optim=optim, gm=gm)
np.testing.assert_almost_equal(
optim._state[net.a]["momentum_buffer"].numpy(), 2.34)
# do 3 steps of infer
for _ in range(3):
loss = eval_func(data)
np.testing.assert_almost_equal(loss.numpy(), 2.34 * (1.23 - 2.34),
5)
np.testing.assert_almost_equal(
optim._state[net.a]["momentum_buffer"].numpy(), 2.34)
# do a step of train
train_func(data, model=net, optim=optim, gm=gm)
np.testing.assert_almost_equal(loss.numpy(), 2.34 * (1.23 - 2.34), 5)
np.testing.assert_almost_equal(
optim._state[net.a]["momentum_buffer"].numpy(), 0.9 * 2.34 + 2.34,
5)
def set_b(symbolic, *args):
# print('set_b:', args)
def set_func(inp, val):
for i in mge_idx:
if isinstance(i, (list, Tensor)):
return inp.set_ai(val)[mge_idx]
return inp.set_subtensor(val)[mge_idx]
func = trace(set_func, symbolic=symbolic)
return func(*args)
def get_b(symbolic, *args):
# print('get_b:', args)
def get_func(inp):
for i in mge_idx:
if isinstance(i, (list, Tensor)):
return inp.ai[mge_idx]
return inp[mge_idx]
func = trace(get_func, symbolic=symbolic)
return func(*args)
def run_test(func, args, ref_shape, is_trace, sym=False):
args = [tensor(t, dtype="float32") for t in args]
if is_trace:
func = trace(symbolic=sym)(func)
for _ in range(3):
out = func(*args)
assert out.numpy().shape == ref_shape
else:
out = func(*args)
assert out.numpy().shape == ref_shape, out.numpy().shape
def worker(data, expect):
rank = dist.get_rank()
inp = tensor(data[rank])
def func():
output = scatter(inp, axis=axis)
return output
func = trace(symbolic=symbolic)(func)
output = func()
assert np.allclose(output.numpy(), expect[rank])
def run_train(
model_path,
use_jit,
use_symbolic,
sublinear_memory_config=None,
max_err=None,
use_adaptive_pooling=False,
):
"""
Load the model with test cases and run the training for one iter.
The loss and updated weights are compared with reference value to verify the correctness.
Dump a new file with updated result by calling update_model
if you think the test fails due to numerical rounding errors instead of bugs.
Please think twice before you do so.
"""
net = MnistNet(has_bn=True, use_adaptive_pooling=use_adaptive_pooling)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
gm = ad.GradManager().attach(net.parameters())
data = Tensor(checkpoint["data"], dtype=np.float32)
label = Tensor(checkpoint["label"], dtype=np.int32)
if max_err is None:
max_err = 1e-5
train_func = train
if use_jit:
train_func = jit.trace(
train_func,
symbolic=use_symbolic,
sublinear_memory_config=sublinear_memory_config,
)
opt.clear_grad()
loss = train_func(data, label, net, opt, gm)
opt.step()
np.testing.assert_allclose(loss.numpy(), checkpoint["loss"], atol=max_err)
for param, param_ref in zip(
net.state_dict().items(), checkpoint["net_updated"].items()
):
assert param[0] == param_ref[0]
if "bn" in param[0]:
ref = param_ref[1].reshape(param[1].shape)
np.testing.assert_allclose(param[1], ref, atol=max_err)
else:
np.testing.assert_allclose(param[1], param_ref[1], atol=max_err)
def test_grad_scaler():
def f():
gm = GradManager()
scaler = GradScaler()
x = mge.tensor(1.0)
for _ in range(3):
with gm:
y = x + 1
gm.attach(y)
loss = y + 1
scaler.backward(gm, loss, unscale_grad=False)
np.testing.assert_equal(y.grad.numpy(), scaler.scale_factor)
scaler.unscale(gm.attached_tensors())
np.testing.assert_equal(y.grad.numpy(), 1)
# test handle None elements
scaler.unscale(gm.attached_tensors())
f()
trace(f)()
def _reset_jit_graph(self, impl: callable):
"""We override this func to attach weight clipping after default training step"""
traced_obj = jit.trace(impl)
def _(*args, **kwargs):
ret = traced_obj(*args, **kwargs)
if self.training:
self._apply_lipshitz_constraint(
) # dynamically apply weight clipping
return ret
return _
def test_rng_empty_tensor(is_symbolic):
set_global_seed(1024)
shapes = [
(0, ),
(0, 0, 0),
(10, 0, 10),
]
def fn(shape):
o1 = random.uniform(0, 1, shape)
o2 = random.normal(0, 1, shape)
o3 = random.gamma(2, 1, shape)
o4 = random.beta(2, 1, shape)
o5 = random.poisson(2, shape)
return o1, o2, o3, o4, o5
for shape in shapes:
if is_symbolic is not None:
fn_ = jit.trace(symbolic=is_symbolic)(fn)
else:
fn_ = fn
for _ in range(3):
outs = fn_(shape)
for out in outs:
np.testing.assert_equal(out.numpy().shape, shape)
if is_symbolic is None:
break
def fn2(n):
return random.permutation(n=n)
if is_symbolic is not None:
fn2 = jit.trace(symbolic=is_symbolic)(fn2)
for _ in range(3):
out = fn2(0)
np.testing.assert_equal(out.numpy().shape, (0, ))
if is_symbolic is None:
break
mge.core.set_option("async_level", 2)
def run(use_trace, symbolic):
a = tensor(np.array([1926.0817], dtype=np.float32))
net = Sigmoid()
func_run = run_saved_context
if use_trace:
func_run = trace(run_saved_context, symbolic=symbolic)
s = func_run(a, net=net)
s2 = F.sigmoid(a)
assertTensorClose(s.numpy(), s2.numpy())
assertTensorClose(
F.grad(s, a, use_virtual_grad=False).numpy(),
F.grad(s2, a, use_virtual_grad=False).numpy(),
)
def run_index(index):
inp, outp = get_param(cases, index)
inp_tensor = [make_tensor(inpi, network) for inpi in inp]
if test_trace and not network:
copied_inp = inp_tensor.copy()
for symbolic in [False, True]:
traced_func = trace(symbolic=symbolic)(func)
for _ in range(3):
traced_results = traced_func(*copied_inp, **kwargs)
check_results(traced_results, outp)
dumped_func = trace(symbolic=True, capture_as_const=True)(func)
dumped_results = dumped_func(*copied_inp, **kwargs)
check_results(dumped_results, outp)
file = io.BytesIO()
dump_info = dumped_func.dump(file)
file.seek(0)
# arg_name has pattern arg_xxx, xxx is int value
def take_number(arg_name):
return int(arg_name.split("_")[-1])
input_names = dump_info[4]
inps_np = [i.numpy() for i in copied_inp]
input_names.sort(key=take_number)
inp_dict = dict(zip(input_names, inps_np))
infer_cg = cgtools.GraphInference(file)
# assume #outputs == 1
loaded_results = list(infer_cg.run(inp_dict=inp_dict).values())[0]
check_results(loaded_results, outp,
check_shape=False) # scalar info lost
results = func(*inp_tensor, **kwargs)
check_results(results, outp, check_shape=(network is None))
def test_pixel_shuffle_symbolic(is_symbolic, type):
def fn(inp, upscale_factor):
return F.pixel_shuffle(inp, upscale_factor=upscale_factor)
if is_symbolic is not None:
fn = jit.trace(symbolic=is_symbolic)(fn)
inp = tensor(np.arange(3 * 4 * 5 * 5).reshape(3, 4, 5, 5).astype(type))
golden = pixel_shuffle(inp, 2)
for _ in range(3):
out = fn(inp, 2)
np.testing.assert_equal(out.numpy(), golden)
if is_symbolic is None:
break
def test_matmul_empty_tensor(shape_a, shape_b, is_symbolic):
def func(a, b):
return F.matmul(a, b)
if is_symbolic is not None:
func = jit.trace(symbolic=is_symbolic)(func)
a = tensor(np.random.randn(*shape_a))
b = tensor(np.random.randn(*shape_b))
for _ in range(3):
out = func(a, b)
assert np.all(out.numpy() == 0)
if is_symbolic is None:
break
def test_batchnorm_change_batchsize():
data_shape = (2, 3, 8, 8)
real_shape = (4, 3, 8, 8)
data = np.random.random(data_shape).astype(np.float32)
d = np.random.random(real_shape).astype(np.float32)
bn = BatchNorm2d(3)
f = trace(bn)
f(data)
y1 = f(d)
y0 = bn(tensor(d))
assertTensorClose(y0.numpy(), y1.numpy())
def test_copy_empty(shape, device_src, device_dst, is_symbolic):
inp = tensor(np.random.randn(*shape).astype("float32"), device=device_src)
def func(inp):
return F.copy(inp, device_dst)
if is_symbolic is not None:
func = trace(symbolic=is_symbolic)(func)
for _ in range(3):
out = func(inp)
assert out.numpy().shape == shape
assert out.device == device_dst
if is_symbolic is None:
break
def test_jit_trace():
module = MyModule()
module.eval()
x = F.ones((1, 8, 14, 14))
expect = module(x)
traced_module = trace_module(module, x)
func = trace(traced_module, capture_as_const=True)
np.testing.assert_array_equal(func(x), expect)
model = io.BytesIO()
func.dump(model)
model.seek(0)
infer_cg = cgtools.GraphInference(model)
np.testing.assert_allclose(list(infer_cg.run(x.numpy()).values())[0],
expect,
atol=1e-6)
def test_roll_empty_tensor(shape, shifts, axis, is_symbolic):
inp = tensor(np.random.randn(*shape).astype("float32"))
def func(inp):
return F.roll(inp, shifts, axis)
if is_symbolic is not None:
func = trace(symbolic=is_symbolic)(func)
out_ref = np.roll(inp.numpy(), shifts, axis)
for _ in range(3):
out = F.roll(inp, shifts, axis)
np.testing.assert_equal(out.numpy(), out_ref)
if is_symbolic is None:
break
def worker():
x = F.ones([3, 10], dtype="float32")
m = M.Linear(10, 10)
def func():
with GradManager().attach(m.parameters()) as gm:
if dist.get_rank() == 0:
y = m(x)
else:
y = x
y = F.distributed.broadcast(y)
gm.backward(y)
if trace_mode is not None:
func = trace(symbolic=trace_mode)(func)
func()
def worker():
m = M.Linear(10, 10)
x = F.ones([3, 10], dtype="float32")
def func():
with GradManager().attach(m.parameters()) as gm:
y = m(x)
y = F.distributed.reduce_sum(y)
if dist.get_rank() == 0:
loss = (2 * y + 1).mean()
gm.backward(loss)
else:
gm.backward()
if trace_mode is not None:
func = trace(symbolic=trace_mode)(func)
func()
def run_test(
model_path, use_jit, use_symbolic, sublinear_memory_config=None, max_err=None,
):
"""
Load the model with test cases and run the training for one iter.
The loss and updated weights are compared with reference value to verify the correctness.
Dump a new file with updated result by calling update_model
if you think the test fails due to numerical rounding errors instead of bugs.
Please think twice before you do so.
"""
net = MnistNet(has_bn=True)
checkpoint = mge.load(model_path)
net.load_state_dict(checkpoint["net_init"])
lr = checkpoint["sgd_lr"]
opt = SGD(net.parameters(), lr=lr)
data = tensor(dtype=np.float32)
label = tensor(dtype=np.int32)
data.set_value(checkpoint["data"])
label.set_value(checkpoint["label"])
if max_err is None:
max_err = 1e-5
train_func = train
if use_jit:
train_func = jit.trace(
train_func,
symbolic=use_symbolic,
sublinear_memory_config=sublinear_memory_config,
)
opt.zero_grad()
loss = train_func(data, label, net=net, opt=opt)
opt.step()
assertTensorClose(loss.numpy(), checkpoint["loss"], max_err=max_err)
for param, param_ref in zip(
net.state_dict().items(), checkpoint["net_updated"].items()
):
assert param[0] == param_ref[0]
assertTensorClose(param[1], param_ref[1], max_err=max_err)
def test_PermutationRNG(symbolic):
m1 = RNG(seed=111, device="xpu0")
m2 = RNG(seed=111, device="xpu1")
m3 = RNG(seed=222, device="xpu0")
out1 = m1.permutation(1000)
out1_ = m1.uniform(size=(1000, ))
out2 = m2.permutation(1000)
out3 = m3.permutation(1000)
np.testing.assert_allclose(out1.numpy(), out2.numpy(), atol=1e-6)
assert out1.device == "xpu0" and out2.device == "xpu1"
assert not (out1.numpy() == out3.numpy()).all()
assert not (out1.numpy() == out1_.numpy()).all()
out = m1.permutation(1000)
out_shp = out.shape
if isinstance(out_shp, tuple):
assert out_shp == (1000, )
else:
assert all(out.shape.numpy() == np.array([1000]))
def sum_result(res, fun):
return sum(
[1 if i == v else 0 for i, v in enumerate(fun(res.numpy()))])
assert sum_result(out, lambda x: x) < 500
assert sum_result(out, np.sort) == 1000
def func():
out = m1.permutation(Tensor(7))
out_shp = out.shape
if isinstance(out_shp, tuple):
assert out_shp == (1, )
else:
assert all(out.shape.numpy() == np.array([1]))
n, m = 6, 3
out = m1.permutation(
Tensor(np.arange(n * m), dtype="float32").reshape(n, m))
out_shp = out.shape
if isinstance(out_shp, tuple):
assert out_shp == (n, m)
else:
assert all(out.shape.numpy() == np.array([n, m]))
func = trace(symbolic=symbolic)(func)
func()
def run_frozen_bn(BNModule, is_training, use_trace, use_symbolic):
nchannel = 3
m = BNModule(nchannel, freeze=True)
if is_training:
m.train()
else:
m.eval()
var = 4.0
bias = 1.0
shape = (1, nchannel, 1, 1)
m.running_var[...] = var * F.ones(shape)
m.running_mean[...] = bias * F.ones(shape)
saved_var = m.running_var.numpy()
saved_mean = m.running_mean.numpy()
saved_wt = m.weight.numpy()
saved_bias = m.bias.numpy()
gm = ad.GradManager().attach(m.parameters())
optim = optimizer.SGD(m.parameters(), lr=1.0)
optim.clear_grad()
data = np.random.random((6, nchannel, 2, 2)).astype("float32")
def train_fn(d):
for _ in range(3):
with gm:
loss = m(d).mean()
gm.backward(loss)
optim.step()
return loss
if use_trace:
train_fn = trace(train_fn, symbolic=use_symbolic)
for _ in range(3):
loss = train_fn(megengine.tensor(data))
if not is_training:
np.testing.assert_equal(m.running_var.numpy(), saved_var)
np.testing.assert_equal(m.running_mean.numpy(), saved_mean)
np.testing.assert_almost_equal(
loss.numpy(), ((data - bias) / np.sqrt(var)).mean(), 5
)
np.testing.assert_equal(m.weight.numpy(), saved_wt)
np.testing.assert_equal(m.bias.numpy(), saved_bias)
def worker(data):
rank = dist.get_rank()
inp = tensor(data[rank])
def func():
all_to_all_output = all_to_all(inp,
split_axis=split_axis,
concat_axis=concat_axis)
gather_C = gather(inp, axis=concat_axis)
gather_B = gather(all_to_all_output, axis=split_axis)
if rank == 0:
return gather_B, gather_C
return all_to_all_output
func = trace(symbolic=symbolic)(func)
ret = func()
if rank == 0:
assert np.allclose(ret[0], ret[1])
def test_profiler(format, trace_mode):
tempdir = tempfile.TemporaryDirectory()
profile_prefix = tempdir.name
profile_path = os.path.join(profile_prefix,
"{}.{}".format(os.getpid(), format))
def infer():
with scope("my_scope"):
oup = Simple()(tensor([1.23], dtype="float32"))
return oup
if trace_mode:
infer = trace(symbolic=trace_mode)(infer)
with Profiler(profile_prefix, format=format):
infer()
print(profile_path)
assert os.path.exists(profile_path), "profiling results not found"
if format == "chrome_timeline.json":
with open(profile_path, "r") as f:
events = json.load(f)
if isinstance(events, dict):
assert "traceEvents" in events
events = events["traceEvents"]
prev_ts = {}
scope_count = 0
for event in events:
if "dur" in event:
assert event["dur"] >= 0
elif "ts" in event and "tid" in event:
ts = event["ts"]
tid = event["tid"]
if ts != 0:
assert (tid not in prev_ts) or prev_ts[tid] <= ts
prev_ts[tid] = ts
if "name" in event and event["name"] == "my_scope":
scope_count += 1
assert scope_count > 0 and scope_count % 2 == 0
def test_broadcast_on_empty_tensor(is_trace):
input1_shape = (100, 0, 1)
output1_shape = (100, 0, 10)
data1 = tensor(np.random.random(input1_shape).astype(np.float32))
input2_shape = (10, 0)
output2_shape = (10, 10, 0)
data2 = tensor(np.random.random(input2_shape).astype(np.float32))
input3_shape = (0, 0, 1, 10)
output3_shape = (10, 0, 0, 10, 10)
data3 = tensor(np.random.random(input3_shape).astype(np.float32))
def comp(out, target_shp):
assert out._tuple_shape == target_shp
def func(x, shp):
return F.broadcast_to(x, shp)
cases = [
[data1, output1_shape],
[data2, output2_shape],
[data3, output3_shape],
]
def test(func, inp, comp, target_shp):
out = func(inp, target_shp)
comp(out, target_shp)
if is_trace:
for symbolic in [False, True]:
for inp, target_shp in cases:
func_traced = trace(symbolic=symbolic)(func)
test(func_traced, inp, comp, target_shp)
test(func_traced, inp, comp, target_shp)
test(func_traced, inp, comp, target_shp)
else:
for inp, target_shp in cases:
test(func, inp, comp, target_shp)
def test_batchnorm_empty_tensor(dim, is_symbolic):
if dim == 1:
m = BatchNorm1d(4, affine=True)
inp = mge.tensor(np.random.randn(0, 4, 0).astype("float32"))
elif dim == 2:
m = BatchNorm2d(4, affine=True)
inp = mge.tensor(np.random.randn(0, 4, 0, 0).astype("float32"))
else:
raise NotImplementedError
m.train()
def fn(inp):
return m(inp)
if is_symbolic is not None:
fn = jit.trace(symbolic=is_symbolic)(fn)
for _ in range(3):
out = fn(inp)
np.testing.assert_equal(out.numpy(), inp)
if is_symbolic is None:
break
def test_trace_advance_indexing(shape_mode):
funcs = [
lambda x, i: x[i],
# lambda x, i, j: x[i, j], # FIXME
lambda x, i, j: x[i, :, j, ...],
# lambda x, start, end: x[start:end], # FIXME
lambda x, start, end: x[:, 0, start:end, ..., 1],
lambda x, vec: x[vec],
lambda x, vec: x[vec, ..., 0, 1:3],
lambda x, vec: x[vec, vec[0], vec[1]],
# lambda x, i, start, end, vec: x[i, ..., :, vec, start:end], # FIXME
lambda x, mask: x[mask],
]
inputs = {
"x": np.random.randn(5, 5, 5, 5, 5).astype("float32"),
"i": 0,
"j": 2,
"start": 1,
"end": 3,
"vec": [1, 2, 3],
"mask": np.random.randn(5, 5, 5, 5, 5) >= 0,
}
for f in funcs:
sig = inspect.signature(f)
param_names = list(sig._parameters.keys())
params = {}
params_np = {}
f_traced = trace(f, symbolic=False, symbolic_shape=shape_mode)
for name in param_names:
params[name] = tensor(inputs[name])
params_np[name] = inputs[name]
expected = f(**params_np)
result_imperative = f(**params)
np.testing.assert_equal(expected, result_imperative.numpy())
for _ in range(3):
result_trace = f_traced(**params)
np.testing.assert_equal(expected, result_trace.numpy())
def test_sort_empty(is_symbolic):
data_shapes = [
(0,),
(10, 0),
]
def fn(x):
return F.sort(x)
for shape in data_shapes:
if is_symbolic is not None:
fn_ = jit.trace(symbolic=is_symbolic)(fn)
else:
fn_ = fn
data = np.random.random(shape).astype(np.float32)
for _ in range(3):
outs = fn_(tensor(data))
ref_outs = (np.sort(data), np.argsort(data))
assert len(ref_outs) == len(outs)
for i in range(len(outs)):
np.testing.assert_equal(outs[i].numpy(), ref_outs[i])
if is_symbolic is None:
break
def test_nms(is_symbolic):
def fn(inp, scores):
return F.vision.nms(
inp,
scores=scores,
iou_thresh=0.5,
max_output=None if is_symbolic is None else 4,
)
if is_symbolic is not None:
fn = jit.trace(symbolic=is_symbolic)(fn)
x = np.array(
[
[0, 0, 100, 100],
[10, 10, 100, 100],
[50, 50, 100, 100],
[100, 100, 150, 150],
],
dtype=np.float32,
)
inp = tensor(x)
scores = tensor([0.5, 0.8, 0.9, 0.6], dtype=np.float32)
for _ in range(3):
result = fn(inp, scores=scores)
np.testing.assert_equal(result.numpy(),
np.array([2, 1, 3], dtype=np.int32))
x = np.array(
[],
dtype=np.float32,
).reshape(0, 4)
inp = tensor(x)
scores = tensor([], dtype=np.float32)
for _ in range(3):
result = fn(inp, scores=scores)
np.testing.assert_equal(result.numpy(), np.array([], dtype=np.int32))