def build_modality(cls, vocab_size, emb_dim, name, timing=None,
share_embedding_and_softmax_weights=False):
""" Creates modality layer.
Args:
vocab_size: An integer, the vocabulary size.
emb_dim: An integer, the dimension of the embedding.
timing: A string or a dict of parameter for positional embedding parameters.
name: A string, the layer name.
share_embedding_and_softmax_weights: Whether to share the embedding table and softmax weight.
Returns:
A modality layer.
"""
modality = WordEmbeddingSharedWeights(
embedding_dim=emb_dim, vocab_size=vocab_size,
share_softmax_weights=share_embedding_and_softmax_weights,
name=name)
# position embedding wrapper
if timing:
if isinstance(timing, str):
timing = {"timing": timing}
elif not isinstance(timing, dict):
raise ValueError("Unknown type of timing params: {}".format(str(timing)))
modality = PositionEmbeddingWrapper(
embedding_layer=modality, name=name + "_posenc_wrapper", **timing)
return modality
def test_emb():
emb_dim = 5
vocab_size = 10
tf_emb = WordEmbeddingSharedWeights(emb_dim, vocab_size, True)
pt_emb = PTWordEmbeddingSharedWeights(emb_dim, vocab_size, True)
inp_2d = numpy.random.randint(0, 9, [2, 5])
inp_1d = numpy.random.randint(0, 9, [
3,
])
logits_2d = numpy.random.rand(2, 5)
logits_3d = numpy.random.rand(2, 4, 5)
tf_inp_2d = tf.convert_to_tensor(inp_2d, tf.int32)
tf_inp_1d = tf.convert_to_tensor(inp_1d, tf.int32)
tf_logits_2d = tf.convert_to_tensor(logits_2d, tf.float32)
tf_logits_3d = tf.convert_to_tensor(logits_3d, tf.float32)
pt_inp_2d = torch.IntTensor(inp_2d)
pt_inp_1d = torch.IntTensor(inp_1d)
pt_logits_2d = torch.FloatTensor(logits_2d)
pt_logits_3d = torch.FloatTensor(logits_3d)
_ = tf_emb(tf_logits_2d, mode="linear")
_ = pt_emb(pt_logits_2d, mode="linear")
pt_emb._shared_weights.data = torch.Tensor(tf_emb._shared_weights.numpy())
pt_emb._bias.data = torch.Tensor(tf_emb._bias.numpy())
assert_equal_numpy(
tf_emb(tf_logits_2d, mode="linear").numpy(),
pt_emb(pt_logits_2d, mode="linear").detach().numpy())
assert_equal_numpy(
tf_emb(tf_logits_3d, mode="linear").numpy(),
pt_emb(pt_logits_3d, mode="linear").detach().numpy())
assert_equal_numpy(
tf_emb(tf_inp_2d).numpy(),
pt_emb(pt_inp_2d).detach().numpy())
assert_equal_numpy(
tf_emb(tf_inp_1d).numpy(),
pt_emb(pt_inp_1d).detach().numpy())
def new(cls, args: dict, vocab_meta, name=None):
""" Builds a sequence to sequence model.
Args:
args: A dict containing all model parameters.
vocab_meta: A dict containing source-side vocabulary meta data, e.g. eos_id, vocab_size.
name: The name of the model.
Returns:
A GPT2 model.
"""
embedding = WordEmbeddingSharedWeights(
embedding_dim=args["hidden_size"],
vocab_size=vocab_meta["vocab_size"],
share_softmax_weights=True,
use_bias=args["softmax_bias"],
name="embeddings")
timing = args["timing"]
if timing:
if isinstance(timing, str):
timing = {"timing": timing}
elif not isinstance(timing, dict):
raise ValueError("Unknown type of timing params: {}".format(
str(timing)))
embedding = PositionEmbeddingWrapper(embedding_layer=embedding,
name="posenc_wrapper",
**timing)
decoder = TransformerDecoder(
num_layers=args["num_layers"],
hidden_size=args["hidden_size"],
num_attention_heads=args["num_attention_heads"],
filter_size=args["filter_size"],
ffn_activation=args["ffn_activation"],
attention_dropout_rate=args["attention_dropout_rate"],
attention_type=args["attention_type"],
ffn_dropout_rate=args["ffn_dropout_rate"],
layer_postprocess_dropout_rate=args[
"layer_postprocess_dropout_rate"],
layer_postprocess_epsilon=args["layer_postprocess_epsilon"],
no_cross_attn_layer_list=[i for i in range(args["num_layers"])],
name="decoder")
model = cls(args, vocab_meta, embedding, decoder, name=name)
_ = model({
"trg":
tf.convert_to_tensor([[0, 1, 2, vocab_meta["pad_id"]]], tf.int64),
"trg_input":
tf.convert_to_tensor([[vocab_meta["bos_id"], 1, 2]], tf.int64),
"trg_length":
tf.convert_to_tensor([4], tf.int64)
})
return model
def test_word_embedding():
embedding_layer = WordEmbeddingSharedWeights(embedding_dim=5,
vocab_size=10,
share_softmax_weights=False)
inputs2d = numpy.random.randint(0, 9, size=(3, 4))
inputs1d = numpy.random.randint(0, 9, size=(3, ))
emb_for_2d = embedding_layer(inputs2d)
emb_for_1d = embedding_layer(inputs1d)
assert len(embedding_layer.get_weights()) == 1
assert numpy.sum(
(embedding_layer.get_weights()[0][inputs1d] - emb_for_1d.numpy())**
2) < 1e-9
assert numpy.sum(
(embedding_layer.get_weights()[0][inputs2d] - emb_for_2d.numpy())**
2) < 1e-9
assert "emb/weights" in embedding_layer.trainable_weights[0].name
emb_shared_layer = WordEmbeddingSharedWeights(embedding_dim=5,
vocab_size=10,
share_softmax_weights=True)
emb_for_2d = emb_shared_layer(inputs2d)
emb_for_1d = emb_shared_layer(inputs1d)
logits_for_2d = emb_shared_layer(emb_for_2d, mode="linear")
logits_for_1d = emb_shared_layer(emb_for_1d, mode="linear")
assert len(emb_shared_layer.get_weights()) == 2
for w in emb_shared_layer.trainable_weights:
if "bias" in w.name:
bias = w
else:
weights = w
assert numpy.sum(
(weights.numpy()[inputs1d] - emb_for_1d.numpy())**2) < 1e-9
assert numpy.sum(
(weights.numpy()[inputs2d] - emb_for_2d.numpy())**2) < 1e-9
assert numpy.sum(
(numpy.dot(emb_for_2d.numpy(), numpy.transpose(weights.numpy())) +
bias.numpy() - logits_for_2d.numpy())**2) < 1e-9
assert numpy.sum(
(numpy.dot(emb_for_1d.numpy(), numpy.transpose(weights.numpy())) +
bias.numpy() - logits_for_1d.numpy())**2) < 1e-9
assert "shared/weights" in weights.name
assert "shared/bias" in bias.name
def test_position_embedding():
tf_postbl = PositionEmbeddingWrapper.add_sinusoids_timing_signal(
tf.zeros([1, 10, 10]), None)
pt_postbl = PTPositionEmbeddingWrapper.add_sinusoids_timing_signal(
torch.zeros(1, 10, 10), None)
assert_equal_numpy(tf_postbl.numpy(), pt_postbl.detach().numpy())
emb_dim = 5
vocab_size = 10
tf_emb = WordEmbeddingSharedWeights(emb_dim, vocab_size, False)
pt_emb = PTWordEmbeddingSharedWeights(emb_dim, vocab_size, False)
inp_2d = numpy.random.randint(0, 9, [2, 5])
inp_1d = numpy.random.randint(0, 9, [
3,
])
logits_2d = numpy.random.rand(2, 5)
logits_3d = numpy.random.rand(2, 4, 5)
tf_inp_2d = tf.convert_to_tensor(inp_2d, tf.int32)
tf_inp_1d = tf.convert_to_tensor(inp_1d, tf.int32)
tf_logits_2d = tf.convert_to_tensor(logits_2d, tf.float32)
tf_logits_3d = tf.convert_to_tensor(logits_3d, tf.float32)
pt_inp_2d = torch.IntTensor(inp_2d)
pt_inp_1d = torch.IntTensor(inp_1d)
pt_logits_2d = torch.FloatTensor(logits_2d)
pt_logits_3d = torch.FloatTensor(logits_3d)
_ = tf_emb(tf_logits_2d, mode="linear")
_ = pt_emb(pt_logits_2d, mode="linear")
pt_emb._shared_weights.data = torch.Tensor(tf_emb._shared_weights.numpy())
tf_posemb = PositionEmbeddingWrapper("sinusoids", tf_emb)
pt_posemb = PTPositionEmbeddingWrapper("sinusoids", pt_emb)
assert_equal_numpy(
tf_posemb(tf_logits_2d, mode="linear").numpy(),
pt_posemb(pt_logits_2d, mode="linear").detach().numpy())
assert_equal_numpy(
tf_posemb(tf_logits_3d, mode="linear").numpy(),
pt_posemb(pt_logits_3d, mode="linear").detach().numpy())
assert_equal_numpy(
tf_posemb(tf_inp_2d).numpy(),
pt_posemb(pt_inp_2d).detach().numpy())
assert_equal_numpy(
tf_posemb(tf_inp_1d, time=5).numpy(),
pt_posemb(pt_inp_1d, time=5).detach().numpy())
def new(cls, args: dict, vocab_meta, name=None):
""" Builds a sequence to sequence model.
Args:
args: A dict containing all model parameters.
vocab_meta: A dict containing source-side vocabulary meta data, e.g. eos_id, vocab_size.
name: The name of the model.
Returns:
A GPT2 model.
"""
# build source and target modality
embedding = PositionEmbeddingWrapper(
embedding_layer=WordEmbeddingSharedWeights(
embedding_dim=args["hidden_size"],
vocab_size=vocab_meta["vocab_size"],
share_softmax_weights=True,
use_bias=False,
name="embeddings"),
name="posenc_wrapper",
timing="emb",
max_positions=args["max_position_embeddings"])
decoder = TransformerDecoder(
num_layers=args["num_layers"],
hidden_size=args["hidden_size"],
num_attention_heads=args["num_attention_heads"],
filter_size=args["filter_size"],
ffn_activation=args["ffn_activation"],
attention_dropout_rate=args["attention_dropout_rate"],
attention_type=args["attention_type"],
ffn_dropout_rate=args["ffn_dropout_rate"],
layer_postprocess_dropout_rate=args[
"layer_postprocess_dropout_rate"],
layer_postprocess_epsilon=args["layer_postprocess_epsilon"],
with_encoder_decoder_attention=False,
name="decoder")
model = cls(args, vocab_meta, embedding, decoder, name=name)
_ = model({"tokens": tf.convert_to_tensor([[0, 1, 2]], tf.int64)})
return model
def test_position_embedding():
embedding_layer = WordEmbeddingSharedWeights(embedding_dim=5,
vocab_size=10,
share_softmax_weights=False)
embedding_layer = PositionEmbeddingWrapper(
timing="sinusoids",
embedding_layer=embedding_layer,
)
inputs1d = tf.convert_to_tensor([4, 7, 8], tf.int32)
inputs2d = tf.convert_to_tensor([[3, 1, 1, 1], [8, 1, 6, 4], [6, 6, 0, 5]],
tf.int32)
_ = embedding_layer(inputs2d)
assert len(embedding_layer.get_weights()) == 1
assert "emb/weights" in embedding_layer.trainable_weights[0].name
embedding_layer.set_weights([
numpy.array(
[[-0.22683287, 0.20732224, -0.10953838, 0.15318757, -0.07203472],
[0.48726183, 0.53683335, 0.38046378, -0.42776877, 0.51263684],
[-0.20618078, 0.43871957, 0.26764846, 0.57276505, -0.13321346],
[0.34847826, 0.1998071, 0.48136407, -0.03138721, -0.5397158],
[-0.31466845, 0.24504018, 0.38156456, -0.03245735, 0.28105468],
[-0.4769836, -0.2763745, -0.35024986, 0.5304734, -0.2523746],
[0.13987714, -0.36480358, 0.5633767, 0.04371119, -0.5429846],
[0.07482189, 0.4224295, 0.5645891, -0.12718052, 0.3637674],
[0.4379062, 0.11231863, -0.6134181, -0.53932106, -0.5402442],
[-0.18054467, -0.21964127, -0.14727849, 0.61441237, -0.13402274]])
])
emb_for_2d = embedding_layer(inputs2d)
emb_for_1d = embedding_layer(inputs1d, time=3)
assert numpy.sum((emb_for_2d.numpy() - numpy.array(
[[[0.77922106, 0.4467823, 2.0763628, 0.92981607, -1.2068413],
[1.9310216, 1.2004958, 1.3910451, 0.04347992, 1.1462909],
[1.998848, 1.2005959, 0.43459606, 0.04347992, 1.1462909],
[1.2306706, 1.2006959, -0.13924962, 0.04347986, 1.1462909]],
[[0.9791881, 0.2511521, -0.37164462, -0.2059586, -1.2080228],
[1.9310216, 1.2004958, 1.3910451, 0.04347992, 1.1462909],
[1.2220722, -0.81552565, 0.8436019, 1.0977412, -1.2141505],
[-0.56250006, 0.5482265, -0.13678819, 0.9274231, 0.62845737]],
[[0.3127748, -0.8157256, 2.2597487, 1.0977412, -1.2141505],
[1.1542457, -0.8156256, 1.800051, 1.0977412, -1.2141505],
[0.4020837, 0.46378663, -0.6610821, 1.3425379, -0.16107452],
[-0.92544776, -0.6176922, -1.773175, 2.1861746, -0.56432676]]]))**
2) < 1e-9
assert numpy.sum((emb_for_1d.numpy() - numpy.array(
[[-0.56250006, 0.5482265, -0.13678819, 0.9274231, 0.62845737],
[0.30842686, 0.9448811, 0.27246714, 0.71561563, 0.8134086],
[1.120308, 0.2514521, -2.361637, -0.20595866, -1.2080228]]))**
2) < 1e-9
emb_shared_layer = WordEmbeddingSharedWeights(embedding_dim=5,
vocab_size=10,
share_softmax_weights=True)
emb_shared_layer = PositionEmbeddingWrapper(
timing="emb", embedding_layer=emb_shared_layer)
emb_for_2d = emb_shared_layer(inputs2d)
logits_for_2d = emb_shared_layer(emb_for_2d, mode="linear")
assert len(emb_shared_layer.get_weights()) == 3
for w in emb_shared_layer.trainable_weights:
if "shared/weights" in w.name:
weights = w
elif "shared/bias" in w.name:
bias = w
assert numpy.sum(
(numpy.dot(emb_for_2d.numpy(), numpy.transpose(weights.numpy())) +
bias.numpy() - logits_for_2d.numpy())**2) < 1e-9