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test_models_albert.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.albert import AlbertModel, AlbertForMLM, AlbertForPretrain,\
list_pretrained_albert, get_pretrained_albert
mx.npx.set_np()
def get_test_cfg():
vocab_size = 500
num_token_types = 3
num_layers = 3
num_heads = 2
units = 64
hidden_size = 96
hidden_dropout_prob = 0.0
attention_dropout_prob = 0.0
cfg = AlbertModel.get_cfg().clone()
cfg.defrost()
cfg.MODEL.vocab_size = vocab_size
cfg.MODEL.num_token_types = num_token_types
cfg.MODEL.units = units
cfg.MODEL.hidden_size = hidden_size
cfg.MODEL.num_heads = num_heads
cfg.MODEL.num_layers = num_layers
cfg.MODEL.hidden_dropout_prob = hidden_dropout_prob
cfg.MODEL.attention_dropout_prob = attention_dropout_prob
return cfg
@pytest.mark.parametrize('static_alloc,static_shape', [(False, False),
(True, True)])
@pytest.mark.parametrize('compute_layout', ['auto', 'NT', 'TN'])
def test_albert_backbone(static_alloc, static_shape, compute_layout):
batch_size = 3
cfg = get_test_cfg()
cfg.defrost()
cfg.MODEL.compute_layout = compute_layout
cfg.freeze()
model = AlbertModel.from_cfg(cfg, use_pooler=True)
model.initialize()
model.hybridize(static_alloc=static_alloc, static_shape=static_shape)
cfg_tn = cfg.clone()
cfg_tn.defrost()
cfg_tn.MODEL.layout = 'TN'
cfg_tn.freeze()
model_tn = AlbertModel.from_cfg(cfg_tn, use_pooler=True)
model_tn.share_parameters(model.collect_params())
model_tn.hybridize(static_alloc=static_alloc, static_shape=static_shape)
for seq_length in [64, 96]:
valid_length = mx.np.random.randint(seq_length // 2, seq_length, (batch_size,))
inputs = mx.np.random.randint(0, cfg.MODEL.vocab_size, (batch_size, seq_length))
token_types = mx.np.random.randint(0, cfg.MODEL.num_token_types, (batch_size, seq_length))
contextual_embedding, pooled_out = model(inputs, token_types, valid_length)
contextual_embedding_tn, pooled_out_tn = model_tn(inputs.T, token_types.T, valid_length)
# Verify layout
assert_allclose(np.swapaxes(contextual_embedding_tn.asnumpy(), 0, 1),
contextual_embedding.asnumpy(), 1E-4, 1E-4)
assert_allclose(pooled_out_tn.asnumpy(), pooled_out.asnumpy(), 1E-4, 1E-4)
assert contextual_embedding.shape == (batch_size, seq_length, cfg.MODEL.units)
assert pooled_out.shape == (batch_size, cfg.MODEL.units)
# Ensure the embeddings that exceed valid_length are masked
contextual_embedding_np = contextual_embedding.asnumpy()
pooled_out_np = pooled_out.asnumpy()
for i in range(batch_size):
ele_valid_length = valid_length[i].asnumpy()
assert_allclose(contextual_embedding_np[i, ele_valid_length:],
np.zeros_like(contextual_embedding_np[i, ele_valid_length:]),
1E-5, 1E-5)
# Ensure that the content are correctly masked
new_inputs = mx.np.concatenate([inputs, inputs[:, :5]], axis=-1)
new_token_types = mx.np.concatenate([token_types, token_types[:, :5]], axis=-1)
new_contextual_embedding, new_pooled_out = \
model(new_inputs, new_token_types, valid_length)
new_contextual_embedding_np = new_contextual_embedding.asnumpy()
new_pooled_out_np = new_pooled_out.asnumpy()
for i in range(batch_size):
ele_valid_length = valid_length[i].asnumpy()
assert_allclose(new_contextual_embedding_np[i, :ele_valid_length],
contextual_embedding_np[i, :ele_valid_length], 1E-5, 1E-5)
assert_allclose(new_pooled_out_np, pooled_out_np, 1E-4, 1E-4)
@pytest.mark.parametrize('compute_layout', ['auto', 'NT', 'TN'])
@mx.use_np
def test_albert_for_mlm_model(compute_layout):
batch_size = 3
cfg = get_test_cfg()
cfg.defrost()
cfg.MODEL.compute_layout = compute_layout
cfg.freeze()
albert_mlm_model = AlbertForMLM(backbone_cfg=cfg)
albert_mlm_model.initialize()
albert_mlm_model.hybridize()
cfg_tn = cfg.clone()
cfg_tn.defrost()
cfg_tn.MODEL.layout = 'TN'
cfg_tn.freeze()
albert_mlm_tn_model = AlbertForMLM(backbone_cfg=cfg_tn)
albert_mlm_tn_model.share_parameters(albert_mlm_model.collect_params())
albert_mlm_tn_model.hybridize()
num_mask = 16
seq_length = 64
inputs = mx.np.random.randint(0, cfg.MODEL.vocab_size, (batch_size, seq_length))
token_types = mx.np.random.randint(0, cfg.MODEL.num_token_types, (batch_size, seq_length))
valid_length = mx.np.random.randint(seq_length // 2, seq_length, (batch_size,))
masked_positions = mx.np.random.randint(0, seq_length // 2, (batch_size, num_mask))
contextual_embeddings, pooled_out, mlm_scores = albert_mlm_model(inputs, token_types, valid_length, masked_positions)
contextual_embeddings_tn, pooled_out_tn, mlm_scores_tn = albert_mlm_tn_model(inputs.T, token_types.T, valid_length, masked_positions)
assert_allclose(np.swapaxes(contextual_embeddings_tn.asnumpy(), 0, 1),
contextual_embeddings.asnumpy(), 1E-4, 1E-4)
assert_allclose(pooled_out_tn.asnumpy(), pooled_out.asnumpy(), 1E-4, 1E-4)
assert_allclose(mlm_scores_tn.asnumpy(), mlm_scores.asnumpy(), 1E-4, 1E-4)
assert mlm_scores.shape == (batch_size, num_mask, cfg.MODEL.vocab_size)
@pytest.mark.parametrize('compute_layout', ['auto', 'NT', 'TN'])
def test_albert_for_pretrain_model(compute_layout):
batch_size = 3
cfg = get_test_cfg()
cfg.defrost()
cfg.MODEL.compute_layout = compute_layout
cfg.freeze()
albert_pretrain_model = AlbertForPretrain(backbone_cfg=cfg)
albert_pretrain_model.initialize()
albert_pretrain_model.hybridize()
cfg_tn = cfg.clone()
cfg_tn.defrost()
cfg_tn.MODEL.layout = 'TN'
cfg_tn.freeze()
albert_pretrain_model_tn = AlbertForPretrain(backbone_cfg=cfg_tn)
albert_pretrain_model_tn.share_parameters(albert_pretrain_model.collect_params())
albert_pretrain_model_tn.hybridize()
num_mask = 16
seq_length = 64
inputs = mx.np.random.randint(0, cfg.MODEL.vocab_size, (batch_size, seq_length))
token_types = mx.np.random.randint(0, cfg.MODEL.num_token_types, (batch_size, seq_length))
valid_length = mx.np.random.randint(seq_length // 2, seq_length, (batch_size,))
masked_positions = mx.np.random.randint(0, seq_length // 2, (batch_size, num_mask))
contextual_embeddings, pooled_out, sop_score, mlm_scores =\
albert_pretrain_model(inputs, token_types, valid_length, masked_positions)
contextual_embeddings_tn, pooled_out_tn, sop_score_tn, mlm_scores_tn = \
albert_pretrain_model_tn(inputs.T, token_types.T, valid_length, masked_positions)
assert_allclose(np.swapaxes(contextual_embeddings_tn.asnumpy(), 0, 1),
contextual_embeddings.asnumpy(), 1E-4, 1E-4)
assert_allclose(pooled_out_tn.asnumpy(), pooled_out.asnumpy(), 1E-4, 1E-4)
assert_allclose(sop_score.asnumpy(), sop_score_tn.asnumpy(), 1E-4, 1E-4)
assert_allclose(mlm_scores.asnumpy(), mlm_scores_tn.asnumpy(), 1E-4, 1E-4)
assert mlm_scores.shape == (batch_size, num_mask, cfg.MODEL.vocab_size)
assert sop_score.shape == (batch_size, 2)
def test_list_pretrained_albert():
assert len(list_pretrained_albert()) > 0
@pytest.mark.slow
@pytest.mark.remote_required
@pytest.mark.parametrize('model_name', list_pretrained_albert())
def test_albert_get_pretrained(model_name):
assert len(list_pretrained_albert()) > 0
with tempfile.TemporaryDirectory() as root:
cfg, tokenizer, backbone_params_path, mlm_params_path =\
get_pretrained_albert(model_name, load_backbone=True, load_mlm=True, root=root)
assert cfg.MODEL.vocab_size == len(tokenizer.vocab)
albert_model = AlbertModel.from_cfg(cfg)
albert_model.load_parameters(backbone_params_path)
albert_mlm_model = AlbertForMLM(cfg)
if mlm_params_path is not None:
albert_mlm_model.load_parameters(mlm_params_path)
# Just load the backbone
albert_mlm_model = AlbertForMLM(cfg)
albert_mlm_model.backbone_model.load_parameters(backbone_params_path)