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test_models_transformer.py
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test_models_transformer.py
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import numpy as np
import mxnet as mx
import pytest
from numpy.testing import assert_allclose
from gluonnlp.models.transformer import\
TransformerEncoder, TransformerDecoder, \
TransformerModel, TransformerInference,\
transformer_cfg_reg
from gluonnlp.attention_cell import gen_mem_attn_mask, gen_self_attn_mask
from gluonnlp.utils.testing import verify_nmt_model, verify_nmt_inference
from gluonnlp.utils.testing import verify_backbone_fp16
from gluonnlp.utils.parameter import count_parameters, deduplicate_param_dict
mx.npx.set_np()
@pytest.mark.parametrize('pre_norm', [False, True])
@pytest.mark.parametrize('num_enc_layers', [2, 3])
@pytest.mark.parametrize('num_dec_layers', [2, 3])
def test_transformer_encoder_decoder(pre_norm, num_enc_layers, num_dec_layers):
batch_size = 8
src_seq_length = 20
tgt_seq_length = 15
units = 32
enc = TransformerEncoder(units=units, hidden_size=64, num_layers=num_enc_layers, num_heads=4,
dropout=0.0, pre_norm=pre_norm)
dec = TransformerDecoder(units=units, hidden_size=64, num_layers=num_dec_layers, num_heads=4,
dropout=0.0, pre_norm=pre_norm)
enc.hybridize()
# disabled due to two different signatures calling attention_cell in this test
# dec.hybridize()
enc.initialize()
dec.initialize()
src_data = mx.np.random.normal(0, 1, (batch_size, src_seq_length, units))
src_valid_length = mx.np.random.randint(1, src_seq_length, (batch_size,))
dst_data = mx.np.random.normal(0, 1, (batch_size, tgt_seq_length, units))
dst_valid_length = mx.np.random.randint(5, tgt_seq_length, (batch_size,))
encoded_mem = enc(src_data, src_valid_length)
full_decode_out = dec(dst_data, dst_valid_length, encoded_mem, src_valid_length)
# Test for the TN layout
enc_tn = TransformerEncoder(units=units, hidden_size=64, num_layers=num_enc_layers, num_heads=4,
dropout=0.0, pre_norm=pre_norm, layout='TN')
enc_tn.share_parameters(enc.collect_params())
dec_tn = TransformerDecoder(units=units, hidden_size=64, num_layers=num_dec_layers, num_heads=4,
dropout=0.0, pre_norm=pre_norm, layout='TN')
dec_tn.share_parameters(dec.collect_params())
enc_tn.hybridize()
dec_tn.hybridize()
encoded_mem_tn = enc_tn(mx.np.swapaxes(src_data, 0, 1), src_valid_length)
full_decode_out_tn = dec_tn(mx.np.swapaxes(dst_data, 0, 1), dst_valid_length,
encoded_mem_tn, src_valid_length)
assert_allclose(encoded_mem_tn.asnumpy(),
mx.np.swapaxes(encoded_mem, 0, 1).asnumpy(), 1E-5, 1E-5)
assert_allclose(full_decode_out_tn.asnumpy(),
mx.np.swapaxes(full_decode_out, 0, 1).asnumpy(), 1E-5, 1E-5)
# Test the consistency via shifting the data and the valid_length
for i in range(1, dst_valid_length.asnumpy().min()):
for partial_decode_out in [dec(dst_data[:, :(-i), :],
dst_valid_length - i, encoded_mem, src_valid_length),
dec(dst_data, dst_valid_length - i,
encoded_mem, src_valid_length)]:
for b in range(batch_size):
vl = dst_valid_length.asnumpy()[b] - i
assert_allclose(partial_decode_out.asnumpy()[b, :vl, :],
full_decode_out.asnumpy()[b, :vl, :], 1E-5, 1E-5)
# Test the decoder layer
self_causal_mask = gen_self_attn_mask(dst_data, dst_valid_length, attn_type='causal')
mem_attn_mask = gen_mem_attn_mask(encoded_mem, src_valid_length, dst_data, dst_valid_length)
enc_mem_attn_mask = gen_mem_attn_mask(encoded_mem, src_valid_length, dst_data[:, 0:1, :],
None)
print(enc_mem_attn_mask)
h_out = dec.layers[0](dst_data, encoded_mem, self_causal_mask, mem_attn_mask)
states = dec.layers[0].init_states(batch_size, h_out.ctx, h_out.dtype)
h_out_from_incremental = []
for i in range(tgt_seq_length):
ele_h_out, states = dec.layers[0].incremental_decode(dst_data[:, i, :], states,
encoded_mem, src_valid_length,
enc_mem_attn_mask)
h_out_from_incremental.append(ele_h_out)
h_out_from_incremental = mx.np.stack(h_out_from_incremental, axis=1)
for i in range(batch_size):
val_length = dst_valid_length[i].asnumpy()
assert_allclose(h_out_from_incremental[i, :val_length, :].asnumpy(),
h_out[i, :val_length, :].asnumpy(), 1E-5, 1E-5)
# Test for the full decoder
states = dec.init_states(batch_size, src_data.ctx, src_data.dtype)
final_out_from_incremental = []
for i in range(tgt_seq_length):
ele_final_out, states = dec.incremental_decode(dst_data[:, i, :],
states, encoded_mem, src_valid_length)
final_out_from_incremental.append(ele_final_out)
final_out_from_incremental = mx.np.stack(final_out_from_incremental, axis=1)
for i in range(batch_size):
val_length = dst_valid_length[i].asnumpy()
assert_allclose(final_out_from_incremental[i, :val_length, :].asnumpy(),
full_decode_out[i, :val_length, :].asnumpy(), 1E-5, 1E-5)
@pytest.mark.parametrize('train_hybridize,inference_hybridize',
[(False, False), (False, True), (True, True)])
@pytest.mark.parametrize('enc_pre_norm,dec_pre_norm',
[(False, False), (True, True)])
@pytest.mark.parametrize('enc_num_layers,dec_num_layers,enc_units,dec_units',
[(2, 2, 24, 24),
(2, 3, 16, 24)])
@pytest.mark.parametrize('enc_recurrent', [False, True])
@pytest.mark.parametrize('dec_recurrent', [False, True])
@pytest.mark.parametrize('tie_weights,layout', [(False, 'NT'), (True, 'NT'), (True, 'TN')])
def test_transformer_inference(train_hybridize, inference_hybridize,
enc_pre_norm, dec_pre_norm,
enc_units, dec_units,
enc_num_layers, dec_num_layers,
enc_recurrent, dec_recurrent, tie_weights,
layout):
src_seq_length = 20
tgt_seq_length = 15
src_vocab_size = 32
tgt_vocab_size = 32
if enc_units != dec_units:
shared_embed = False
else:
shared_embed = True
model = TransformerModel(src_vocab_size=src_vocab_size,
tgt_vocab_size=tgt_vocab_size,
max_src_length=src_seq_length,
max_tgt_length=tgt_seq_length,
enc_units=enc_units,
enc_hidden_size=64,
enc_num_heads=4,
enc_num_layers=enc_num_layers,
enc_pre_norm=enc_pre_norm,
enc_recurrent=enc_recurrent,
dec_units=dec_units,
dec_hidden_size=64,
dec_num_heads=4,
dec_num_layers=dec_num_layers,
dec_pre_norm=dec_pre_norm,
dec_recurrent=dec_recurrent,
shared_embed=shared_embed,
tie_weights=tie_weights,
dropout=0.0,
layout=layout)
inference_model = TransformerInference(model=model)
model.initialize()
if train_hybridize:
model.hybridize()
verify_nmt_model(model)
if inference_hybridize:
inference_model.hybridize()
verify_nmt_inference(train_model=model, inference_model=inference_model)
def test_transformer_cfg_registry():
assert len(transformer_cfg_reg.list_keys()) > 0
@pytest.mark.parametrize('cfg_key', transformer_cfg_reg.list_keys())
def test_transformer_cfg(cfg_key):
cfg = TransformerModel.get_cfg(cfg_key)
cfg.defrost()
cfg.MODEL.src_vocab_size = 32
cfg.MODEL.tgt_vocab_size = 32
cfg.freeze()
model = TransformerModel.from_cfg(cfg)
model.initialize()
model.hybridize()
cfg.defrost()
cfg.MODEL.layout = 'TN'
cfg.freeze()
model_tn = TransformerModel.from_cfg(cfg)
model_tn.share_parameters(model.collect_params())
model_tn.hybridize()
mx.npx.waitall()
@pytest.mark.parametrize('enc_pre_norm,dec_pre_norm',
[(False, False), (True, True)])
@pytest.mark.parametrize('enc_num_layers,dec_num_layers,enc_units,dec_units',
[(2, 2, 24, 24),
(2, 3, 16, 16)])
@pytest.mark.parametrize('enc_recurrent', [False, True])
@pytest.mark.parametrize('dec_recurrent', [False, True])
@pytest.mark.parametrize('tie_weights,layout', [(False, 'NT'), (True, 'NT'), (True, 'TN')])
def test_transformer_fp16_amp(enc_pre_norm, dec_pre_norm,
enc_units, dec_units,
enc_num_layers, dec_num_layers,
enc_recurrent, dec_recurrent, tie_weights,
layout, ctx):
if ctx.device_type != 'gpu':
pytest.skip('Only test amp when running on GPU.')
# Generate configuration for testing
cfg = TransformerModel.get_cfg()
cfg.defrost()
cfg.MODEL.src_vocab_size = 32
cfg.MODEL.tgt_vocab_size = 32
cfg.MODEL.max_src_length = 20
cfg.MODEL.max_tgt_length = 15
cfg.MODEL.tie_weights = tie_weights
cfg.MODEL.layout = layout
# Encoder config
cfg.MODEL.ENCODER.pre_norm = enc_pre_norm
cfg.MODEL.ENCODER.units = enc_units
cfg.MODEL.ENCODER.num_layers = enc_num_layers
cfg.MODEL.ENCODER.recurrent = enc_recurrent
# Decoder config
cfg.MODEL.DECODER.pre_norm = dec_pre_norm
cfg.MODEL.DECODER.units = dec_units
cfg.MODEL.DECODER.num_layers = dec_num_layers
cfg.MODEL.DECODER.recurrent = dec_recurrent
cfg.freeze()
batch_size = 4
seq_length = 16
with ctx:
if layout == 'NT':
src_data = mx.np.random.randint(0, cfg.MODEL.src_vocab_size,
(batch_size, seq_length), dtype=np.int32)
src_valid_length = mx.np.random.randint(seq_length // 2, seq_length,
(batch_size,), dtype=np.int32)
tgt_data = mx.np.random.randint(0, cfg.MODEL.tgt_vocab_size,
(batch_size, seq_length), dtype=np.int32)
tgt_valid_length = mx.np.random.randint(seq_length // 2, seq_length,
(batch_size,), dtype=np.int32)
elif layout == 'TN':
src_data = mx.np.random.randint(0, cfg.MODEL.src_vocab_size,
(seq_length, batch_size), dtype=np.int32)
src_valid_length = mx.np.random.randint(seq_length // 2, seq_length,
(batch_size,), dtype=np.int32)
tgt_data = mx.np.random.randint(0, cfg.MODEL.tgt_vocab_size,
(seq_length, batch_size), dtype=np.int32)
tgt_valid_length = mx.np.random.randint(seq_length // 2, seq_length,
(batch_size,), dtype=np.int32)
else:
raise NotImplementedError
verify_backbone_fp16(TransformerModel, cfg, ctx,
inputs=[src_data, src_valid_length, tgt_data, tgt_valid_length])
@pytest.mark.parametrize('cfg_name,gt_num_params,gt_num_fixed_params',
[('transformer_base', 60897280, 512),
('transformer_wmt_en_de_big', 209874944, 1024)])
def test_transformer_param_number(cfg_name, gt_num_params, gt_num_fixed_params):
cfg = TransformerModel.get_cfg(cfg_name)
cfg.defrost()
cfg.MODEL.src_vocab_size = 32768
cfg.MODEL.tgt_vocab_size = 32768
cfg.freeze()
model = TransformerModel.from_cfg(cfg)
model.initialize()
num_params, num_fixed_params = count_parameters(model.collect_params())
assert num_params == gt_num_params
assert num_fixed_params == gt_num_fixed_params
num_params2, num_fixed_params2 = count_parameters(deduplicate_param_dict(model.collect_params()))
assert num_params2 == gt_num_params
assert num_fixed_params2 == gt_num_fixed_params