def test_maml_update_var():
model = OmniglotFC(28 * 28, 5)
model.train()
loss_fn = F.cross_entropy_with_softmax
old_params = list(model.parameters())
maml = MAML(model)
params = list(maml.named_parameters.values())
optimizer = optim.SGD(old_params, lr=0.05)
optimizer.zero_grad()
support_out = model.forward(
meg.tensor(np.random.randn(5, 28 * 28), dtype='float32'))
support_loss = loss_fn(
support_out, meg.tensor(np.random.randint(0, 5, (5)), dtype='int32'))
optimizer.backward(support_loss)
optimizer.step()
assert id(old_params[0]) == id(params[0])
# 手动update
grads = F.grad(support_loss, params, use_virtual_grad=False)
fast_weights = [p - 0.5 * g for g, p in zip(grads, params)]
named_update = dict(zip(maml.named_parameters.keys(), fast_weights))
named_old = dict(zip(maml.named_parameters.keys(), old_params))
maml.replace_parameter(maml.module_table, named_update)
# 被替换为新的值后就无法通过model.parameters()找到了。
after_params = list(model.parameters())
maml.module_table['classifier'].bias
named_update['classifier.bias']
mods = list(model.modules())
mods[1].bias
maml.replace_parameter(maml.module_table, named_old)
def run_syncbn(trace_mode):
x = F.ones([2, 16, 4, 4], dtype="float32")
net = Sequential(
Conv2d(16, 16, 1), SyncBatchNorm(16), Conv2d(16, 16, 1), SyncBatchNorm(16),
)
gm = ad.GradManager().attach(
net.parameters(), callbacks=dist.make_allreduce_cb("MEAN")
)
opt = optimizer.SGD(net.parameters(), 1e-3)
def train_func(x):
with gm:
y = net(x)
loss = y.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
if trace_mode is not None:
train_func = trace(train_func, symbolic=trace_mode)
for _ in range(3):
loss = train_func(x)
loss.numpy()
def test_output_copy_trace():
class Simple(Module):
def __init__(self):
super().__init__()
self.a = Parameter([1.0], dtype=np.float32)
def forward(self, x):
x = x * self.a
# will result into a copy of output in grad
x = F.exp(x)
return x
ys = {False: [], True: []}
for symbolic in [False, True]:
net = Simple()
gm = GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
@trace(symbolic=symbolic)
def train_func(d):
with gm:
loss = net(d)
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = train_func(data).numpy()
ys[symbolic].append(y)
for i in range(3):
np.testing.assert_equal(ys[False][i], ys[True][i])
def test_none_in_out_grad():
class Test(Function):
def forward(self, a, b):
return a, b
def backward(self, grad_a, grad_b):
assert grad_b is None
return (grad_a, None)
class Simple(Module):
def __init__(self, a, b):
super().__init__()
self.a = Parameter(a, dtype=np.float32)
self.b = Parameter(b, dtype=np.float32)
self.layer = Test()
def forward(self):
aa, bb = self.layer(self.a, self.b)
return aa, bb
a = tensor(np.array([1.0], dtype=np.float32))
b = tensor(np.array([2.0], dtype=np.float32))
net = Simple(a, b)
optim = optimizer.SGD(net.parameters(), lr=1.0)
gm = ad.GradManager().attach(net.parameters())
optim.clear_grad()
with gm:
loss, _ = net()
gm.backward(loss)
optim.step()
np.testing.assert_almost_equal(net.a.numpy(),
np.array([1.0 - 1.0], dtype=np.float32))
np.testing.assert_almost_equal(net.b.numpy(),
np.array([2.0 - 0.0], dtype=np.float32))
def test_single_input():
data_shape = (9, 2, 6)
av = np.random.random(data_shape).astype(np.float32)
class MulFunc(Function):
def forward(self, a):
self.a = a
return a * 10
def backward(self, grad_o):
return grad_o * 10
class Simple(Module):
def __init__(self, a):
super().__init__()
self.a = Parameter(a, dtype=np.float32)
self.layer1 = MulFunc()
def forward(self):
x = self.layer1(self.a)
return x
net = Simple(av)
gm = ad.GradManager().attach(net.parameters())
opt = optimizer.SGD(net.parameters(), lr=1.0)
opt.clear_grad()
with gm:
loss = net()
gm.backward(loss.sum())
opt.step()
np.testing.assert_almost_equal(loss.numpy(), (av * 10))
np.testing.assert_almost_equal(net.a.numpy(), (av - 10))
def run(step, enable_trace, use_symbolic):
def train_func(data, net=None, opt=None):
pred = net(data)
opt.backward(pred)
return pred
if enable_trace:
train_func = trace(train_func, symbolic=use_symbolic)
net = Mixed()
data = tensor()
opt = optimizer.SGD(net.parameters(), lr=lr)
saved_param = init_param
for i in range(step):
opt.zero_grad()
data.set_value([i + 1.0])
output = train_func(data, net=net, opt=opt)
opt.step()
expect_param = (
saved_param[0] - lr * saved_param[1] * data.numpy(),
saved_param[1] - lr * saved_param[0] * data.numpy(),
)
assertTensorClose(
output.numpy(), saved_param[0] * saved_param[1] * data.numpy()
)
torch_param = net.torch_module._torch_params[0].detach().cpu()
assertTensorClose(torch_param.numpy(), expect_param[0])
assertTensorClose(net.multiplier.numpy(), expect_param[1])
saved_param = expect_param
def test_Clone_model():
# 必须要将新参数clone到另一个模型中，才可以继续
train_loader = build_dataloader()
image_support = meg.tensor(dtype='float32')
label_support = meg.tensor(dtype="int32")
model = OmniglotFC(28 * 28, 5)
model.train()
loss_fn = F.cross_entropy_with_softmax
optimizer = optim.SGD(model.parameters(), lr=0.05)
iters = iter(train_loader)
(images_support, labels_support, images_query, labels_query) = next(iters)
i = 0
image_support.set_value(images_support[i])
label_support.set_value(labels_support[i])
image_support = F.remove_axis(image_support, 1)
label_support = F.remove_axis(label_support, 1)
support_out = model.forward(image_support)
support_loss = loss_fn(support_out, label_support)
# 对需要梯度更新的参数进行更新
params = list(model.parameters(requires_grad=True))
params[0] = meg.tensor(np.ones((5)), dtype='float32')
grads = F.grad(support_loss, params, use_virtual_grad=False)
fast_weights = [p - 0.5 * g for g, p in zip(grads, params)]
def test_sgd_momentum():
net = Simple()
optim = optimizer.SGD(net.parameters(), lr=1.0, momentum=0.9)
optim.clear_grad()
gm = ad.GradManager().attach(net.parameters())
data = tensor([2.34])
# do a step of train
with gm:
loss = net(data)
gm.backward(loss)
optim.step()
np.testing.assert_almost_equal(
optim._state[net.a]["momentum_buffer"].numpy(), 2.34)
# do a step of infer
loss = net(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
optim.clear_grad()
with gm:
loss = net(data)
gm.backward(loss)
optim.step()
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)
def test_grad_twice_method_3():
# model define
model = CustomModel3()
model.train()
named_param = dict(list(model.named_parameters(requires_grad=True)))
params = list(named_param.values())
external_params = [
meg.Parameter(np.random.normal(size=p.shape), dtype='float32')
for p in params
]
loss_fn = F.cross_entropy_with_softmax
optimizer = optim.SGD(external_params, lr=0.003)
# forward once
optimizer.zero_grad()
x1 = meg.tensor(np.random.randn(5, 10), dtype='float32')
y1 = meg.tensor(np.random.randint(0, 5, (5)), dtype='int32')
x2 = meg.tensor(np.random.randn(5, 10), dtype='float32')
y2 = meg.tensor(np.random.randint(0, 5, (5)), dtype='int32')
train_func3(x1,
y1,
x2,
y2,
loss_fn=loss_fn,
opt=optimizer,
net=model,
params=external_params)
optimizer.step()
def test_grad_twice_method_2():
# model define
model = CustomModel()
model.train()
named_param = dict(list(model.named_parameters(requires_grad=True)))
name_keys = list(named_param.keys())
params = list(named_param.values())
loss_fn = F.cross_entropy_with_softmax
optimizer = optim.SGD(params, lr=0.003)
# forward once
optimizer.zero_grad()
x1 = meg.tensor(np.random.randn(5, 10), dtype='float32')
y1 = meg.tensor(np.random.randint(0, 5, (5)), dtype='int32')
x2 = meg.tensor(np.random.randn(5, 10), dtype='float32')
y2 = meg.tensor(np.random.randint(0, 5, (5)), dtype='int32')
train_func(x1,
y1,
x2,
y2,
loss_fn=loss_fn,
opt=optimizer,
net=model,
keys=name_keys,
params=params)
optimizer.step()
def test_grad_twice():
# model define
model = M.Sequential(M.Linear(10, 20), M.Linear(20, 10), M.Linear(10, 5))
model.train()
named_param = dict(list(model.named_parameters(requires_grad=True)))
named_module = dict(list(model.named_children()))
name_keys = list(named_param.keys())
params = list(named_param.values())
loss_fn = F.cross_entropy_with_softmax
optimizer = optim.SGD(params, lr=0.003)
# forward once
optimizer.zero_grad()
x1 = meg.tensor(np.random.randn(5, 10), dtype='float32')
y1 = meg.tensor(np.random.randint(0, 5, (5)), dtype='int32')
loss = loss_fn(model(x1), y1)
grads = F.grad(loss,
params,
use_virtual_grad=False,
return_zero_for_nodep=False)
fast_weights = [p - 0.5 * g for g, p in zip(grads, params)]
# manual update params
replace_parameter(named_module, dict(zip(name_keys, fast_weights)))
# forward twice
x2 = meg.tensor(np.random.randn(5, 10), dtype='float32')
y2 = meg.tensor(np.random.randint(0, 5, (5)), dtype='int32')
loss2 = loss_fn(model(x2), y2)
# got error
replace_parameter(named_module, named_param)
optimizer.backward(loss2)
optimizer.step()
def test_clear_grad():
class StopGradient(Function):
def forward(self, a):
return a
def backward(self, *_):
return None
class Simple(Module):
def __init__(self, a):
super().__init__()
self.a = Parameter(a, dtype=np.float32)
self.layer = StopGradient()
def forward(self):
b = self.a * 3.0
c = self.a * 4.0
return self.layer(b) + c
a = tensor(np.array([1.0], dtype=np.float32))
net = Simple(a)
optim = optimizer.SGD(net.parameters(), lr=1.0)
gm = ad.GradManager().attach(net.parameters())
optim.clear_grad()
with gm:
loss = net()
gm.backward(loss.sum())
optim.step()
np.testing.assert_almost_equal(
net.a.numpy(),
np.array([1.0 - 4.0], dtype=np.float32),
)
def worker():
rank = dist.get_rank()
size = dist.get_world_size()
x = mge.tensor(np.random.randn(1, rank * 2 + 2), dtype=np.float32)
m = M.Linear(rank * 2 + 2, rank * 2 + 4)
gm = GradManager().attach(m.parameters())
opt = optim.SGD(m.parameters(), 1e-3, momentum=0.9)
def train_func(x):
with gm:
if rank != 0:
x = dist.functional.remote_recv(rank - 1,
shape=(1, rank * 2 + 2),
dtype=np.float32)
y = m(x)
if rank != size - 1:
dist.functional.remote_send(y, dest_rank=rank + 1)
gm.backward()
else:
y = y.mean()
gm.backward(y)
opt.step().clear_grad()
train_funcs = [
train_func,
trace(symbolic=False)(train_func),
trace(symbolic=True)(train_func),
]
for func in train_funcs:
for i in range(3):
func(x)
def worker(rank, world_size, args):
if world_size > 1:
dist.init_process_group(
master_ip="localhost",
master_port=23456,
world_size=world_size,
rank=rank,
dev=rank,
)
logger.info("Init process group for gpu%d done", rank)
sys.path.insert(0, os.path.dirname(args.file))
current_network = importlib.import_module(
os.path.basename(args.file).split(".")[0])
model = current_network.Net(current_network.Cfg(),
batch_size=args.batch_size)
params = model.parameters(requires_grad=True)
model.train()
if rank == 0:
logger.info(get_config_info(model.cfg))
opt = optim.SGD(
params,
lr=model.cfg.basic_lr * world_size * model.batch_size,
momentum=model.cfg.momentum,
weight_decay=model.cfg.weight_decay,
)
if args.weight_file is not None:
weights = mge.load(args.weight_file)
model.backbone.bottom_up.load_state_dict(weights)
logger.info("Prepare dataset")
loader = build_dataloader(model.batch_size, args.dataset_dir, model.cfg)
train_loader = iter(loader["train"])
for epoch_id in range(model.cfg.max_epoch):
for param_group in opt.param_groups:
param_group["lr"] = (model.cfg.basic_lr * world_size *
model.batch_size *
(model.cfg.lr_decay_rate**bisect.bisect_right(
model.cfg.lr_decay_sates, epoch_id)))
tot_steps = model.cfg.nr_images_epoch // (model.batch_size *
world_size)
train_one_epoch(model, train_loader, opt, tot_steps, rank, epoch_id,
world_size)
if rank == 0:
save_path = "log-of-{}/epoch_{}.pkl".format(
os.path.basename(args.file).split(".")[0], epoch_id)
mge.save(
{
"epoch": epoch_id,
"state_dict": model.state_dict()
},
save_path,
)
logger.info("dump weights to %s", save_path)
def test_save_load():
net = Simple()
optim = optimizer.SGD(net.parameters(), lr=1.0, momentum=0.9)
optim.clear_grad()
gm = ad.GradManager().attach(net.parameters())
data = tensor([2.34])
with gm:
loss = net(data)
gm.backward(loss)
optim.step()
model_name = "simple.pkl"
print("save to {}".format(model_name))
mge.save(
{
"name": "simple",
"state_dict": net.state_dict(),
"opt_state": optim.state_dict(),
},
model_name,
)
# Load param to cpu
checkpoint = mge.load(model_name, map_location="cpu0")
device_save = mge.get_default_device()
mge.set_default_device("cpu0")
net = Simple()
net.load_state_dict(checkpoint["state_dict"])
optim = optimizer.SGD(net.parameters(), lr=1.0, momentum=0.9)
optim.load_state_dict(checkpoint["opt_state"])
print("load done")
os.remove("simple.pkl")
with gm:
loss = net([1.23])
gm.backward(loss)
optim.step()
# Restore device
mge.set_default_device(device_save)
def worker(rank, gpu_num, args):
# using sublinear
os.environ[
"MGB_COMP_GRAPH_OPT"] = "enable_sublinear_memory_opt=1;seq_opt.enable_seq_comp_node_opt=0"
os.environ["MGB_SUBLINEAR_MEMORY_GENETIC_NR_ITER"] = '50'
if gpu_num > 1:
dist.init_process_group(
master_ip="localhost",
master_port=23456,
world_size=gpu_num,
rank=rank,
dev=rank,
)
logger.info("Init process group for gpu%d done", rank)
model = network.Network()
params = model.parameters(requires_grad=True)
model.train()
opt = optim.SGD(
params,
lr=cfg.basic_lr * gpu_num * cfg.batch_per_gpu,
momentum=cfg.momentum,
weight_decay=cfg.weight_decay,
)
if cfg.pretrain_weight is not None:
weights = mge.load(cfg.pretrain_weight)
del weights['fc.weight']
del weights['fc.bias']
model.resnet50.load_state_dict(weights)
logger.info("Prepare dataset")
train_loader = dataset.train_dataset(rank)
logger.info("Training...")
for epoch_id in range(cfg.max_epoch):
for param_group in opt.param_groups:
param_group["lr"] = (cfg.basic_lr * gpu_num * cfg.batch_per_gpu *
(cfg.lr_decay_rate**bisect.bisect_right(
cfg.lr_decay_sates, epoch_id)))
max_steps = cfg.nr_images_epoch // (cfg.batch_per_gpu * gpu_num)
train_one_epoch(model, train_loader, opt, max_steps, rank, epoch_id,
gpu_num)
if rank == 0:
save_path = os.path.join(cfg.model_dir,
'epoch_{}.pkl'.format(epoch_id + 1))
mge.save(
{
"epoch": epoch_id,
"state_dict": model.state_dict()
},
save_path,
)
logger.info("dump weights to %s", save_path)
def test_bn_no_track_stat():
nchannel = 3
m = BatchNorm2d(nchannel, track_running_stats=False)
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")
with gm:
loss = m(data).sum()
gm.backward(loss)
optim.step()
def test_hello_world():
net = Simple()
optim = optimizer.SGD(net.parameters(), lr=1.0)
optim.clear_grad()
gm = ad.GradManager().attach(net.parameters())
data = tensor([2.34])
with gm:
loss = net(data)
gm.backward(loss)
optim.step()
np.testing.assert_almost_equal(net.a.numpy(),
np.array([1.23 - 2.34]).astype(np.float32))
def test_optimizer_serialization():
data, data_shape, label, label_shape = get_input()
mlp = MLP()
opt = optimizer.SGD(mlp.parameters(), lr=0.01, momentum=0.9)
slots = TensorDict()
for param in mlp.parameters():
slots[param] = np.zeros(param.shape).astype(np.float32)
pred = mlp(data)
loss = F.square_loss(pred, label.reshape(-1, 1))
opt.zero_grad()
opt.backward(loss)
opt.step()
for param in mlp.parameters():
slots[param] = slots[param] * 0.9 + param.grad.numpy()
with BytesIO() as fout:
save(opt.state_dict(), fout)
fout.seek(0)
state_dict = load(fout)
opt1 = optimizer.SGD(mlp.parameters(), lr=0.02, momentum=0.8)
opt1.load_state_dict(state_dict)
data.set_value(np.random.random(data_shape).astype(np.float32))
label.set_value(np.random.randint(0, 10, label_shape))
pred = mlp(data)
loss = F.square_loss(pred, label.reshape(-1, 1))
opt1.zero_grad()
opt1.backward(loss)
orig_params = TensorDict()
for param in mlp.parameters():
orig_params[param] = np.copy(param.numpy())
opt1.step()
for param in mlp.parameters():
orig_param = orig_params[param]
slots[param] = slots[param] * 0.9 + param.grad.numpy()
assertTensorClose(param.numpy(), orig_param - 0.01 * slots[param])
def train_pipeline():
m = ResNet18Pipeline()
x = F.ones([32, 3, 224, 224])
label = F.zeros([
32,
], dtype="int32")
gm = ad.GradManager().attach(m.parameters())
opt = optim.SGD(m.parameters(), 1e-3, 0.9, 1e-4)
for _ in range(2):
m(x)
loss = m.backward(label, gm)
opt.step().clear_grad()
print(loss)
def test_clip_grad_norm():
net = Net()
x = mge.tensor(np.random.randn(10, 3, 224, 224))
gm = ad.GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
with gm:
loss = net(x).sum()
gm.backward(loss)
save_grad_value(net)
max_norm = 1.0
original_norm = optim.clip_grad_norm(net.parameters(), max_norm=max_norm, ord=2)
scale = max_norm / original_norm
for param in net.parameters():
np.testing.assert_almost_equal(param.grad.numpy(), param.grad_backup * scale)
opt.step().clear_grad()
def test_detach():
net = Simple()
optim = optimizer.SGD(net.parameters(), lr=1.0)
optim.clear_grad()
gm = ad.GradManager().attach(net.parameters())
dshape = (10, 10)
data = tensor(np.ones(dshape).astype(np.float32))
with gm:
loss = net(data).sum()
gm.backward(loss)
optim.step()
np.testing.assert_equal(net.a.numpy(), np.array([1.0]).astype(np.float32))
np.testing.assert_equal(net.b.numpy(),
np.array([1.0 - 10.0 * 10.0]).astype(np.float32))
def test_sgd_momentum_trace():
origin_inplace = os.getenv("MEGENGINE_INPLACE_UPDATE")
symbolic = (True, False)
inplace = (0, 1)
for symbolic, inplace in itertools.product(symbolic, inplace):
os.environ["MEGENGINE_INPLACE_UPDATE"] = str(inplace)
@trace(symbolic=symbolic)
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
@trace(symbolic=symbolic)
def eval_func(data, *, model=None, optim=None, gm=None):
loss = net(data)
return loss
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)
if origin_inplace:
os.environ["MEGENGINE_INPLACE_UPDATE"] = origin_inplace
else:
del os.environ["MEGENGINE_INPLACE_UPDATE"]
def test_advance_indexing_with_subtensor():
net = Simple2()
gm = ad.GradManager().attach(net.parameters())
optim = optimizer.SGD(net.parameters(), lr=1.0)
optim.clear_grad()
dshape = (2, 3, 4, 3, 4, 2)
raw_data = np.arange(576).reshape(dshape).astype(np.float32)
data = tensor(raw_data)
answer = 1.0 - raw_data[1, ..., :, 0:4:2, 0:2].sum()
with gm:
loss = net(data).sum()
gm.backward(loss)
optim.step()
np.testing.assert_almost_equal(net.a.numpy(),
np.array([answer]).astype(np.float32))
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 test_load_state_dict_no_cache(monkeypatch):
with monkeypatch.context() as mk:
mk.setenv("MEGENGINE_INPLACE_UPDATE", "1")
net = Net()
optim = optimizer.SGD(net.parameters(), lr=0.1)
gm = ad.GradManager().attach(net.parameters())
state = {
"fc.weight": np.array([[0]], dtype=np.float32),
"fc.bias": np.array([0.0], dtype=np.float32),
}
net.load_state_dict(state)
images = mge.tensor([[0]], dtype=np.float32)
with gm:
loss = net(images)
gm.backward(loss)
optim.step()
optim.clear_grad()
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 test_advance_indexing():
net = Simple()
gm = ad.GradManager().attach(net.parameters())
optim = optimizer.SGD(net.parameters(), lr=1.0)
optim.clear_grad()
dshape = (10, 10)
raw_data = np.arange(100).reshape(dshape).astype(np.float32)
raw_mask = (np.random.random_sample(dshape) > 0.5).astype(np.bool_)
data = tensor(raw_data)
mask = tensor(raw_mask)
answer = 1.0 - raw_data[raw_mask].sum()
with gm:
loss = net(data, mask).sum()
gm.backward(loss)
optim.step()
np.testing.assert_almost_equal(net.a.numpy(),
np.array([answer]).astype(np.float32))
def test_elemwise_fuse_in_grad(trace_mode):
w = Parameter(np.ones([4, 6]), dtype="float32")
gm = GradManager().attach(w)
opt = optim.SGD([w], lr=0.01, momentum=0.9, weight_decay=5e-4)
# explicitly declare opt_level as 2
@trace(symbolic=trace_mode, opt_level=2)
def f():
with gm:
wm = F.sum(w**2, axis=1)**0.5
loss = wm.mean()
gm.backward(loss)
opt.step().clear_grad()
return loss
for i in range(3):
y = f()
y.numpy()
def test_clip_grad_value():
net = Net()
x = np.random.randn(10, 3, 224, 224).astype("float32")
gm = ad.GradManager().attach(net.parameters())
opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
with gm:
y = net(mge.tensor(x))
y = y.mean()
gm.backward(y)
save_grad_value(net)
max_val = 5
min_val = -2
optim.clip_grad_value(net.parameters(), lower=min_val, upper=max_val)
for param in net.parameters():
np.testing.assert_almost_equal(
param.grad.numpy(),
np.maximum(np.minimum(param.grad_backup, max_val), min_val),
)
opt.step().clear_grad()
