bilma_MX / modeling_bilma.py

Update modeling_bilma.py

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	from transformers import TFPreTrainedModel, PreTrainedTokenizer, BatchEncoding

	from tensorflow.keras.models import Model, load_model, Sequential
	from tensorflow.keras.layers import Layer, Dense, concatenate, Input, add, Dropout, LayerNormalization, MultiHeadAttention, Embedding
	import tensorflow as tf
	import numpy as np

	from typing import Dict

	import re
	import unicodedata

	from .configuration_bilma import BilmaConfig

	# copied from preprocessing.py
	BLANK = ' '

	RE_OPS = re.I \| re.M \| re.S
	RE_USR = re.compile(r"""@\S+""", RE_OPS)
	RE_TAG = re.compile(r"""#\S+""", RE_OPS)
	RE_URL = re.compile(r"""(http\|ftp\|https)://\S+""", RE_OPS)
	RE_NUM = re.compile(r"""[-+]?\d+\.?\d*""", RE_OPS)

	SYMBOLS_ = "()[]¿?¡!{}~<>\|"
	SYMBOLS = set(";:,.@\\-\"/" + SYMBOLS_)



	# ------------------
	# Class declaration
	# ------------------


	class TFBilma(TFPreTrainedModel):
	config_class = BilmaConfig
	main_input_name = "input_ids"
	#base_model_prefix = "bilma"

	def __init__(self, config):
	self.seq_max_length = config.seq_max_length
	self.include_top = config.include_top
	self.add_head = config.add_head
	super().__init__(config)

	self.model = bilma(num_enc=config.num_hidden_layers,
	embed_dim=config.hidden_size,
	max_length=config.seq_max_length,
	num_heads=config.num_attention_heads,
	ff_dim=config.hidden_size,
	vocab_size=config.vocab_size,
	rate=config.hidden_dropout_prob,
	include_top = config.include_top,
	add_head = config.add_head,
	pooling = config.pooling)

	@property
	def dummy_inputs(self) -> Dict[str, tf.Tensor]:

	dummies = {}
	for key, spec in self.input_signature.items():
	dummy_shape = [dim if dim is not None else 2 for dim in spec.shape]
	if spec.shape[0] is None:
	dummy_shape[0] = 1
	dummies[key] = tf.ones(shape=dummy_shape, dtype=spec.dtype)


	return dummies

	@property
	def input_signature(self) -> Dict[str, tf.TensorSpec]:
	sig = {}
	sig["input_ids"] = tf.TensorSpec([None, self.seq_max_length], tf.int32, name="input_ids")
	return sig


	def call(self, inputs):
	if isinstance(inputs, Dict) or isinstance(inputs, BatchEncoding):
	ins = tf.cast(inputs["input_ids"], tf.float32)
	else:
	ins = inputs
	if self.include_top:
	output = {"logits":self.model(ins)}
	else:
	if self.add_head is None:
	output = {"last_hidden_state":self.model(ins)}
	else:
	output = {"label":self.model(ins)}
	return output

	def get_loss_function():
	return loss_funtion()

	def get_acc_function():
	return accuracy_function()


	# copied from bilma_model.py
	# --------------------------

	def loss_function(ignore_id=0):
	loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')
	def loss(real, pred):
	mask = tf.math.logical_not(tf.math.equal(real, ignore_id))
	loss_ = loss_object(real, pred)
	mask = tf.cast(mask, dtype=loss_.dtype)
	loss_ *= mask
	sum_ = tf.reduce_sum(mask,axis=1)

	loss_ = tf.math.divide_no_nan(tf.reduce_sum(loss_, axis=1), sum_)
	return loss_
	return loss

	def accuracy_function(ignore_id=0):
	def acc_mlm(real, pred):
	accuracies = tf.equal(tf.cast(real, tf.int64), tf.argmax(pred, axis=2))

	mask = tf.math.logical_not(tf.math.equal(real, ignore_id))
	accuracies = tf.math.logical_and(mask, accuracies)

	accuracies = tf.cast(accuracies, dtype=tf.float32)
	mask = tf.cast(mask, dtype=tf.float32)
	return tf.math.divide_no_nan(tf.reduce_sum(accuracies), tf.reduce_sum(mask))
	return acc_mlm

	def mean_vectors(inputs, enc_vectors, max_length):
	p = tf.where(inputs == 3)
	pos = tf.transpose(p)[1]
	C = tf.sequence_mask(pos, maxlen=max_length, dtype=tf.float32)
	C = tf.reshape(C, (-1, max_length, 1))
	S = tf.reduce_sum(enc_vectors * C, 1)
	x = S / tf.expand_dims(tf.cast(pos, tf.float32), (1))
	return x

	def mean_diff_vectors(inputs, enc_vectors, max_length):
	p = tf.where(inputs == 3)
	pos = tf.transpose(p)[1]
	C = tf.sequence_mask(pos, maxlen=max_length, dtype=tf.float32)
	C = tf.reshape(C, (-1, max_length, 1))
	vecs = enc_vectors * C
	S = tf.reduce_sum(vecs, 1)
	mu = S / tf.expand_dims(tf.cast(pos, tf.float32), (1))
	x = tf.reduce_sum(mu - vecs, 1) / tf.expand_dims(tf.cast(pos, tf.float32), (1))
	return x

	def max_vectors(inputs, enc_vectors, max_length):
	p = tf.where(inputs == 3)
	pos = tf.transpose(p)[1]
	C = tf.sequence_mask(pos, maxlen=max_length, dtype=tf.float32)
	C = tf.reshape(C, (-1, max_length, 1))
	x = tf.reduce_max(enc_vectors * C, 1)
	return x

	def cls_vectors(inputs, enc_vectors, max_length):
	x = tf.squeeze(enc_vectors[:, 0:1, :], axis=1)
	return x


	def bilma(num_enc=6, embed_dim=300, max_length=50, num_heads=6, ff_dim=512, vocab_size=9739, rate=0.1, include_top=True, add_head=None, pooling=None):
	capt_inputs_ids = Input(shape=(max_length, ), name='input_ids')
	capt_embedding = Embedding(vocab_size, embed_dim, mask_zero=False, name="bilma/embedding")
	capt_inputs = capt_embedding(capt_inputs_ids)

	enc = Encoder(num_enc, embed_dim, max_length, num_heads, ff_dim, rate=rate, name="bilma/encoder")
	enc_output = enc(capt_inputs)
	if include_top:
	fin_output = Dense(vocab_size, use_bias=True, name="bilma/dense_final")(enc_output)
	else:
	x = enc_output
	if pooling == "mean":
	x = mean_vectors(capt_inputs_ids, x, max_length)
	elif pooling == "cls":
	x = cls_vectors(capt_inputs_ids, x, max_length)
	elif pooling == "max":
	x = max_vectors(capt_inputs_ids, x, max_length)

	if add_head is None:
	fin_output = x
	else:
	for i, m in enumerate(add_head[:-1]):
	x = Dense(m, use_bias=True, activation="relu", name=f"bilma/dense_ex_{i}")(x)
	fin_output = Dense(add_head[-1], use_bias=True, activation="softmax", name=f"bilma/dense_ex_final")(x)

	caption_model = Model(inputs=capt_inputs_ids, outputs=fin_output, name="bilma_model")
	return caption_model

	def load(model_file):
	custom_objects={"EncoderBlock": EncoderBlock,
	"Encoder": Encoder,
	"loss": loss_function(),
	"acc_mlm":accuracy_function(),
	}
	return load_model(model_file, custom_objects=custom_objects)


	#
	# Copied from transformer_text.py
	# -------------------------------
	class EncoderBlock(Layer):
	def __init__(self, layer_num, patch_dim, num_heads, ff_dim, rate=0.1, **kwargs):
	super(EncoderBlock, self).__init__(**kwargs)
	self.ln = layer_num
	self.p_d = patch_dim
	self.n_h = num_heads
	self.f_d = ff_dim
	self.rate = rate

	self.att = MultiHeadAttention(num_heads=num_heads, key_dim=patch_dim, name=f"bilma/MHA_{layer_num}")
	self.ffn = Sequential(
	#[Conv1D(ff_dim, kernel_size=1, activation=tf.nn.gelu),
	# Conv1D(patch_dim, kernel_size=1),]
	[Dense(ff_dim, activation=tf.nn.gelu, name=f"bilma/dense1_{layer_num}"),
	Dense(patch_dim, name=f"bilma/dense2_{layer_num}")]
	)
	#self.layernorm0 = LayerNormalization(epsilon=1e-6)
	self.layernorm1 = LayerNormalization(epsilon=1e-6, name=f"ln1_{layer_num}")
	self.layernorm2 = LayerNormalization(epsilon=1e-6, name=f"ln2_{layer_num}")
	self.dropout1 = Dropout(rate)
	self.dropout2 = Dropout(rate)

	def get_config(self):
	config = super(EncoderBlock, self).get_config()
	config.update({"layer_num":self.ln, "patch_dim":self.p_d, "num_heads":self.n_h, "ff_dim":self.f_d, "rate":self.rate})
	return config

	def call(self, inputs, training=False):
	#inputs = self.layernorm0(inputs)
	attn_output = self.att(inputs, inputs)
	attn_output = self.dropout1(attn_output, training=training)
	out1 = self.layernorm1(add([inputs, attn_output]))
	ffn_output = self.ffn(out1)
	ffn_output = self.dropout2(ffn_output, training=training)
	return self.layernorm2(add([out1, ffn_output]))


	class DecoderBlock(Layer):
	def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1, **kwargs):
	super(DecoderBlock, self).__init__(**kwargs)
	self.e_d = embed_dim
	self.n_h = num_heads
	self.f_d = ff_dim
	self.rate = rate

	self.att1 = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
	self.att2 = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
	self.ffn = Sequential(
	#[Conv1D(ff_dim, kernel_size=1, activation=tf.nn.gelu),
	# Conv1D(embed_dim, kernel_size=1),]
	[Dense(ff_dim, activation=tf.nn.gelu),
	Dense(embed_dim),]
	)
	self.layernorm1 = LayerNormalization(epsilon=1e-6)
	self.layernorm2 = LayerNormalization(epsilon=1e-6)
	self.dropout1 = Dropout(rate)
	self.dropout2 = Dropout(rate)
	self.dropout3 = Dropout(rate)

	def get_config(self):
	config = super(DecoderBlock, self).get_config()
	config.update({"embed_dim":self.e_d, "num_heads":self.n_h, "ff_dim":self.f_d, "rate":self.rate})
	return config

	def call(self, inputs, encoder_output, look_ahead_mask, padding_mask, training=None):
	y, attn_output1 = self.att1(inputs, inputs, attention_mask=look_ahead_mask, return_attention_scores=True)
	y = self.dropout1(y, training=training)
	y = add([inputs, y])
	out1 = self.layernorm1(y)

	y, attn_encoder = self.att2(out1, encoder_output, attention_mask=padding_mask, return_attention_scores=True)
	y = self.dropout2(y, training=training)
	y = add([out1, y])
	out2 = self.layernorm1(y)

	ffn_output = self.ffn(out2)
	ffn_output = self.dropout3(ffn_output, training=training)
	final_output = self.layernorm2(out2 + ffn_output)

	return final_output, attn_output1, attn_encoder

	class Encoder(Layer):
	def __init__(self, n, embed_dim, max_length, num_heads, ff_dim, rate=0.1, **kwargs):
	super(Encoder, self).__init__(**kwargs)
	self.n = n
	self.embed_dim = embed_dim
	self.max_length = max_length
	self.n_h = num_heads
	self.f_d = ff_dim
	self.rate = rate
	self._layers = [EncoderBlock(i, embed_dim, num_heads, ff_dim, rate=0.1, name=f"enc_block_{i}") for i in range(n)]
	self.pe = positional_encoding(self.max_length, self.embed_dim)

	def get_config(self):
	config = super(Encoder, self).get_config()
	config.update({"n": self.n, "embed_dim":self.embed_dim, "max_length": self.max_length, "num_heads":self.n_h, "ff_dim":self.f_d, "rate":self.rate})
	return config

	def call(self, x, training=False):
	x *= tf.math.sqrt(tf.cast(self.embed_dim, tf.float32))
	x = x + self.pe[:, :tf.shape(x)[1], :]
	for layer in self._layers:
	x = layer(x, training)
	return x


	class Decoder(Layer):
	def __init__(self, n, embed_dim, max_length, num_heads, ff_dim, rate=0.1, **kwargs):
	super(Decoder, self).__init__(**kwargs)
	self.n = n
	self.embed_dim = embed_dim
	self.max_length = max_length
	self.n_h = num_heads
	self.f_d = ff_dim
	self.rate = rate
	self._layers = [DecoderBlock(embed_dim, num_heads, ff_dim, rate=0.1) for _ in range(n)]
	self.pe = positional_encoding(self.max_length, self.embed_dim)

	def get_config(self):
	config = super(Decoder, self).get_config()
	config.update({"n": self.n, "embed_dim":self.embed_dim, "max_length": self.max_length, "num_heads":self.n_h, "ff_dim":self.f_d, "rate":self.rate})
	return config

	def call(self, x, encoder_output, look_ahead_mask, padding_mask, training):
	x *= tf.math.sqrt(tf.cast(self.embed_dim, tf.float32))
	x = x + self.pe[:, :tf.shape(x)[1], :]

	for layer in self._layers:
	x, self_att, enc_att = layer(x, encoder_output, look_ahead_mask, padding_mask, training)

	return x




	# =========================================
	# M A S K S
	# =========================================
	def create_padding_mask(seq):
	"""
	For self-attention
	seq shape(bs, max_length, emb_dim)
	output shape (bs, max_length, max_length)
	"""
	mask = tf.cast(tf.not_equal(seq, 0), tf.bool)
	mask = tf.reduce_any(mask, 2)
	mask = tf.repeat(mask, seq.shape[1], 0)
	mask = tf.reshape(mask, (-1,seq.shape[1], seq.shape[1]))
	return tf.cast(mask, tf.float32)


	def create_cross_padding_mask(seq, target_seq):
	"""
	For cross-attention
	seq shape(bs, k, image_features)
	target_seq(bs, max_length, emb_dim)
	output shape (bs, max_length, k)
	"""
	mask = tf.cast(tf.not_equal(target_seq, 0), tf.bool)
	mask = tf.reduce_any(mask, 2)
	mask = tf.repeat(mask, seq.shape[1], 0)
	mask = tf.reshape(mask, (-1, tf.shape(seq)[1], tf.shape(target_seq)[1]))
	mask = tf.transpose(mask, [0, 2, 1])
	return mask


	def create_look_ahead_mask(seq):
	"""
	seq shape(bs, max_length, emb_dim)
	output 2D matrix of shape (bs, max_length, max_length) with ones on the diagonal and below.
	"""
	size = seq.shape[1]
	mask = tf.linalg.band_part(tf.ones((size, size)), -1, 0)
	mask = tf.expand_dims(mask, 0)
	mask = tf.repeat(mask, tf.shape(seq)[0], 0)
	return mask


	def create_masks(seq, target_seq):
	decoder_mask = create_padding_mask(target_seq)
	decoder_mask *= create_look_ahead_mask(target_seq)
	cross_att_mask = create_cross_padding_mask(seq, target_seq)
	return decoder_mask, cross_att_mask


	def create_masks_looking_ahead(seq, target_seq):
	decoder_mask = create_padding_mask(target_seq)
	cross_att_mask = create_cross_padding_mask(seq, target_seq)
	return decoder_mask, cross_att_mask

	# =========================================
	# P O S I T I O N A L E N C O D I N G
	# =========================================
	def get_angles(pos, i, d_model):
	angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model))
	return pos * angle_rates

	@tf.autograph.experimental.do_not_convert
	def positional_encoding(position, d_model):
	angle_rads = get_angles(np.arange(position)[:, np.newaxis],
	np.arange(d_model)[np.newaxis, :],
	d_model)

	# apply sin to even indices in the array; 2i
	angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])

	# apply cos to odd indices in the array; 2i+1
	angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])

	pos_encoding = angle_rads[np.newaxis, ...]

	return tf.cast(pos_encoding, dtype=tf.float32)

	class PatchEncoder(Layer):
	def __init__(self, num_patches, projection_dim, **kwargs):
	super(PatchEncoder, self).__init__(**kwargs)
	self.num_patches = num_patches
	self.projection_dim = projection_dim
	self.projection = Dense(units=projection_dim)
	self.position_embedding = Embedding(
	input_dim=num_patches, output_dim=projection_dim
	)

	def get_config(self):
	config = super(PatchEncoder, self).get_config()
	config.update({"num_patches": self.num_patches, "projection_dim":self.projection_dim})
	return config

	def call(self, patch):
	positions = tf.range(start=0, limit=self.num_patches, delta=1)
	encoded = self.projection(patch) + self.position_embedding(positions)
	return encoded