def test_xlmr():
# test from pretrained
assert len(list_pretrained_xlmr()) > 0
for model_name in ['fairseq_xlmr_base']:
with tempfile.TemporaryDirectory() as root:
cfg, tokenizer, params_path, mlm_params_path =\
get_pretrained_xlmr(model_name, load_backbone=True, load_mlm=False, root=root)
assert cfg.MODEL.vocab_size == len(tokenizer.vocab)
# test backbone
xlmr_model = XLMRModel.from_cfg(cfg)
xlmr_model.load_parameters(params_path)
# pass the mlm model
# test forward
batch_size = 1
seq_length = 4
vocab_size = len(tokenizer.vocab)
input_ids = mx.np.array(np.random.randint(2, vocab_size,
(batch_size, seq_length)),
dtype=np.int32)
valid_length = mx.np.array(np.random.randint(seq_length // 2,
seq_length,
(batch_size, )),
dtype=np.int32)
contextual_embeddings, pooled_out = xlmr_model(input_ids, valid_length)
mx.npx.waitall()
# test backward
label_smooth_loss = LabelSmoothCrossEntropyLoss(num_labels=vocab_size)
with mx.autograd.record():
contextual_embeddings, pooled_out = xlmr_model(
input_ids, valid_length)
loss = label_smooth_loss(contextual_embeddings, input_ids)
loss.backward()
mx.npx.waitall()
def test_list_pretrained_xlmr():
assert len(list_pretrained_xlmr()) > 0
import mxnet as mx
import tempfile
from gluonnlp.models.xlmr import XLMRModel, \
list_pretrained_xlmr, get_pretrained_xlmr
from gluonnlp.loss import LabelSmoothCrossEntropyLoss
mx.npx.set_np()
def test_list_pretrained_xlmr():
assert len(list_pretrained_xlmr()) > 0
@pytest.mark.slow
@pytest.mark.remote_required
@pytest.mark.parametrize('model_name', list_pretrained_xlmr())
def test_xlmr(model_name, ctx):
# test from pretrained
assert len(list_pretrained_xlmr()) > 0
with ctx:
with tempfile.TemporaryDirectory() as root:
cfg, tokenizer, params_path, mlm_params_path =\
get_pretrained_xlmr(model_name, load_backbone=True, load_mlm=False, root=root)
assert cfg.MODEL.vocab_size == len(tokenizer.vocab)
# test backbone
xlmr_model = XLMRModel.from_cfg(cfg)
xlmr_model.load_parameters(params_path)
# pass the mlm model
# test forward
batch_size = 1