def test_hred_training_parameters(model, hredmodel):
*xs, y = next(iter(hredmodel.trn_dl))
xs = V(xs)
y = V(y)
optimizer = Adam(model.parameters())
output = model(*xs)
optimizer.zero_grad()
loss = decoder_loss(input=output[0], target=y, pad_idx=hredmodel.pad_idx)
loss.backward()
model_parameters = get_trainable_parameters(model)
grad_flow_parameters = get_trainable_parameters(model, grad=True)
assert set(model_parameters) == set(grad_flow_parameters)
def test_cvae_training_parameters(model, hredmodel, tchebycheff, sigmoid):
*xs, y = next(iter(hredmodel.trn_dl))
xs = V(xs)
y = V(y)
optimizer = Adam(model.parameters())
output = model(*xs)
optimizer.zero_grad()
cvae_loss = get_cvae_loss(pad_idx=hredmodel.pad_idx,
tchebycheff=tchebycheff,
sigmoid=sigmoid)
loss = cvae_loss(input=output[0], target=y)
loss.backward()
model_parameters = get_trainable_parameters(model)
grad_flow_parameters = get_trainable_parameters(model, grad=True)
assert set(model_parameters) == set(grad_flow_parameters)
def test_model(s2smodel):
ntoken = [s2smodel.nt[name] for name in s2smodel.trn_dl.source_names]
model = Transformer(ntoken=ntoken,
max_tokens=5,
eos_token=s2smodel.eos_idx)
model = to_gpu(model)
*xs, y = next(iter(s2smodel.trn_dl))
xs = V(xs)
y = V(y)
optimizer = Adam(model.parameters())
output = model(*xs)
optimizer.zero_grad()
loss = decoder_loss(input=output[0], target=y, pad_idx=s2smodel.pad_idx)
loss.backward()
model_parameters = get_trainable_parameters(model)
grad_flow_parameters = get_trainable_parameters(model, grad=True)
assert set(model_parameters) == set(grad_flow_parameters)