def _run_test(self, requires_grad):
options_file = os.path.join(FIXTURES, 'options.json')
weight_file = os.path.join(FIXTURES, 'lm_weights.hdf5')
embedder = ElmoTokenEmbedder(options_file,
weight_file,
requires_grad=requires_grad)
batch_size = 3
seq_len = 4
char_ids = Variable(
torch.from_numpy(
numpy.random.randint(0, 262, (batch_size, seq_len, 50))))
embeddings = embedder(char_ids)
loss = embeddings.sum()
loss.backward()
elmo_grads = [
param.grad for name, param in embedder.named_parameters()
if '_elmo_lstm' in name
]
if requires_grad:
# None of the elmo grads should be None.
assert all([grad is not None for grad in elmo_grads])
else:
# All of the elmo grads should be None.
assert all([grad is None for grad in elmo_grads])
def _run_test_with_vocab_to_cache(self, requires_grad):
vocab_to_cache = ['<pad>', 'hello', 'world']
embedder = ElmoTokenEmbedder(self.options_file,
self.weight_file,
requires_grad=requires_grad,
vocab_to_cache=vocab_to_cache)
word_tensor = torch.LongTensor([[[1, 2]]])
for _ in range(2):
embeddings = embedder(word_tensor, word_tensor)
loss = embeddings.sum()
loss.backward()
elmo_grads = [param.grad for name, param in embedder.named_parameters()
if '_elmo_lstm' in name and '_token_embedder' not in name]
if requires_grad:
# None of the elmo grads should be None.
assert all([grad is not None for grad in elmo_grads])
else:
# All of the elmo grads should be None.
assert all([grad is None for grad in elmo_grads])
assert all([param.grad is None for name, param in embedder.named_parameters()
if '_token_embedder' in name])
def _run_test(self, requires_grad):
embedder = ElmoTokenEmbedder(self.options_file, self.weight_file, requires_grad=requires_grad)
batch_size = 3
seq_len = 4
char_ids = torch.from_numpy(numpy.random.randint(0, 262, (batch_size, seq_len, 50)))
embeddings = embedder(char_ids)
loss = embeddings.sum()
loss.backward()
elmo_grads = [param.grad for name, param in embedder.named_parameters() if '_elmo_lstm' in name]
if requires_grad:
# None of the elmo grads should be None.
assert all([grad is not None for grad in elmo_grads])
else:
# All of the elmo grads should be None.
assert all([grad is None for grad in elmo_grads])
def _run_test(self, requires_grad):
embedder = ElmoTokenEmbedder(self.options_file, self.weight_file, requires_grad=requires_grad)
batch_size = 3
seq_len = 4
char_ids = torch.from_numpy(numpy.random.randint(0, 262, (batch_size, seq_len, 50)))
for _ in range(2):
embeddings = embedder(char_ids)
loss = embeddings.sum()
loss.backward()
elmo_grads = [param.grad for name, param in embedder.named_parameters() if '_elmo_lstm' in name]
if requires_grad:
# None of the elmo grads should be None.
assert all([grad is not None for grad in elmo_grads])
else:
# All of the elmo grads should be None.
assert all([grad is None for grad in elmo_grads])
