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test_models_mobilebert.py
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test_models_mobilebert.py
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import pytest
import numpy as np
from numpy.testing import assert_allclose
import mxnet as mx
import tempfile
from gluonnlp.models.mobilebert import MobileBertModel, MobileBertForMLM, MobileBertForPretrain,\
list_pretrained_mobilebert, get_pretrained_mobilebert
from gluonnlp.utils.testing import verify_backbone_fp16
mx.npx.set_np()
def test_list_pretrained_mobilebert():
assert len(list_pretrained_mobilebert()) > 0
@pytest.mark.parametrize('compute_layout', ['auto', 'TN', 'NT'])
def test_mobilebert_model_small_cfg(compute_layout, ctx):
with ctx:
cfg = MobileBertModel.get_cfg()
cfg.defrost()
cfg.MODEL.vocab_size = 100
cfg.MODEL.num_layers = 2
cfg.MODEL.hidden_size = 128
cfg.MODEL.num_heads = 2
cfg.MODEL.compute_layout = compute_layout
cfg.freeze()
# Generate TN layout
cfg_tn = cfg.clone()
cfg_tn.defrost()
cfg_tn.MODEL.layout = 'TN'
cfg_tn.freeze()
batch_size = 4
sequence_length = 16
num_mask = 3
inputs = mx.np.random.randint(0, 10, (batch_size, sequence_length))
token_types = mx.np.random.randint(0, 2, (batch_size, sequence_length))
valid_length = mx.np.random.randint(3, sequence_length, (batch_size,))
masked_positions = mx.np.random.randint(0, 3, (batch_size, num_mask))
mobile_bert_model = MobileBertModel.from_cfg(cfg)
mobile_bert_model.initialize()
mobile_bert_model.hybridize()
mobile_bert_model_tn = MobileBertModel.from_cfg(cfg_tn)
mobile_bert_model_tn.share_parameters(mobile_bert_model.collect_params())
mobile_bert_model_tn.hybridize()
contextual_embedding, pooled_out = mobile_bert_model(inputs, token_types, valid_length)
contextual_embedding_tn, pooled_out_tn = mobile_bert_model_tn(inputs.T,
token_types.T, valid_length)
assert_allclose(contextual_embedding.asnumpy(),
np.swapaxes(contextual_embedding_tn.asnumpy(), 0, 1),
1E-3, 1E-3)
assert_allclose(pooled_out.asnumpy(), pooled_out_tn.asnumpy(), 1E-3, 1E-3)
# Test for MobileBertForMLM
mobile_bert_mlm_model = MobileBertForMLM(cfg)
mobile_bert_mlm_model.initialize()
mobile_bert_mlm_model.hybridize()
mobile_bert_mlm_model_tn = MobileBertForMLM(cfg_tn)
mobile_bert_mlm_model_tn.share_parameters(mobile_bert_mlm_model.collect_params())
mobile_bert_model_tn.hybridize()
contextual_embedding, pooled_out, mlm_score = mobile_bert_mlm_model(inputs, token_types,
valid_length,
masked_positions)
contextual_embedding_tn, pooled_out_tn, mlm_score_tn =\
mobile_bert_mlm_model_tn(inputs.T, token_types.T, valid_length, masked_positions)
assert_allclose(contextual_embedding.asnumpy(),
np.swapaxes(contextual_embedding_tn.asnumpy(), 0, 1),
1E-3, 1E-3)
assert_allclose(pooled_out_tn.asnumpy(), pooled_out.asnumpy(), 1E-3, 1E-3)
assert_allclose(mlm_score_tn.asnumpy(), mlm_score.asnumpy(), 1E-3, 1E-3)
# Test for MobileBertForPretrain
mobile_bert_pretrain_model = MobileBertForPretrain(cfg)
mobile_bert_pretrain_model.initialize()
mobile_bert_pretrain_model.hybridize()
mobile_bert_pretrain_model_tn = MobileBertForPretrain(cfg_tn)
mobile_bert_pretrain_model_tn.share_parameters(mobile_bert_pretrain_model.collect_params())
mobile_bert_pretrain_model_tn.hybridize()
contextual_embedding, pooled_out, nsp_score, mlm_score =\
mobile_bert_pretrain_model(inputs, token_types, valid_length, masked_positions)
contextual_embedding_tn, pooled_out_tn, nsp_score_tn, mlm_score_tn = \
mobile_bert_pretrain_model_tn(inputs.T, token_types.T, valid_length, masked_positions)
assert_allclose(contextual_embedding.asnumpy(),
np.swapaxes(contextual_embedding_tn.asnumpy(), 0, 1),
1E-3, 1E-3)
assert_allclose(pooled_out.asnumpy(), pooled_out_tn.asnumpy(), 1E-3, 1E-3)
assert_allclose(nsp_score.asnumpy(), nsp_score_tn.asnumpy(), 1E-3, 1E-3)
assert_allclose(mlm_score.asnumpy(), mlm_score_tn.asnumpy(), 1E-3, 1E-3)
# Test for fp16
if ctx.device_type == 'gpu':
pytest.skip('MobileBERT will have nan values in FP16 mode.')
verify_backbone_fp16(model_cls=MobileBertModel, cfg=cfg, ctx=ctx,
inputs=[inputs, token_types, valid_length])
@pytest.mark.remote_required
@pytest.mark.parametrize('model_name', list_pretrained_mobilebert())
def test_mobilebert_get_pretrained(model_name):
with tempfile.TemporaryDirectory() as root:
cfg, tokenizer, backbone_params_path, mlm_params_path =\
get_pretrained_mobilebert(model_name, load_backbone=True, load_mlm=True, root=root)
assert cfg.MODEL.vocab_size == len(tokenizer.vocab)
mobilebert_model = MobileBertModel.from_cfg(cfg)
mobilebert_model.load_parameters(backbone_params_path)
mobilebert_pretain_model = MobileBertForPretrain(cfg)
if mlm_params_path is not None:
mobilebert_pretain_model.load_parameters(mlm_params_path)
mobilebert_pretain_model = MobileBertForPretrain(cfg)
mobilebert_pretain_model.backbone_model.load_parameters(backbone_params_path)