def __init__(self, hidden_size=768, seq_length=512, num_attention_heads=12, intermediate_size=3072, attention_probs_dropout_prob=0.02, use_one_hot_embeddings=False, initializer_range=0.02, hidden_dropout_prob=0.1, use_relative_positions=False, hidden_act="gelu", compute_type=ts.float32): super(BertEncoderLayer, self).__init__() self.attention = BertSelfAttention( hidden_size=hidden_size, seq_length=seq_length, num_attention_heads=num_attention_heads, attention_probs_dropout_prob=attention_probs_dropout_prob, use_one_hot_embeddings=use_one_hot_embeddings, initializer_range=initializer_range, hidden_dropout_prob=hidden_dropout_prob, use_relative_positions=use_relative_positions, compute_type=compute_type) self.intermediate = layers.Dense(in_channels=hidden_size, out_channels=intermediate_size, activation=hidden_act, weight_init=TruncatedNormal(initializer_range)).to_float(compute_type) self.output = BertOutput(in_channels=intermediate_size, out_channels=hidden_size, initializer_range=initializer_range, dropout_prob=hidden_dropout_prob, compute_type=compute_type)
def init_net_param(network, initialize_mode='TruncatedNormal'): """Init the parameters in net.""" params = network.trainable_params() for p in params: if 'beta' not in p.name and 'gamma' not in p.name and 'bias' not in p.name: if initialize_mode == 'TruncatedNormal': p.set_data( initializer(TruncatedNormal(0.02), p.data.shape, p.data.dtype)) else: p.set_data(initialize_mode, p.data.shape, p.data.dtype)
def __init__(self, config, is_training, num_labels=2, dropout_prob=0.0, use_one_hot_embeddings=False): super(BertSquadModel, self).__init__() if not is_training: config.hidden_dropout_prob = 0.0 config.hidden_probs_dropout_prob = 0.0 self.bert = Bert(config, is_training, use_one_hot_embeddings) self.weight_init = TruncatedNormal(config.initializer_range) self.dense1 = layers.Dense(config.hidden_size, num_labels, weight_init=self.weight_init, has_bias=True).to_float(config.compute_type) self.num_labels = num_labels self.dtype = config.dtype self.log_softmax = P.LogSoftmax(axis=1) self.is_training = is_training
def __init__(self, embedding_size, embedding_shape, use_relative_positions=False, use_token_type=False, token_type_vocab_size=16, use_one_hot_embeddings=False, initializer_range=0.02, max_position_embeddings=512, dropout_prob=0.1): super(EmbeddingPostprocessor, self).__init__() self.use_token_type = use_token_type self.token_type_vocab_size = token_type_vocab_size self.use_one_hot_embeddings = use_one_hot_embeddings self.max_position_embeddings = max_position_embeddings self.embedding_table = Parameter(initializer (TruncatedNormal(initializer_range), [token_type_vocab_size, embedding_size]), name='embedding_table') self.shape_flat = (-1,) self.one_hot = layers.OneHot() self.on_value = Tensor(1.0, ts.float32) self.off_value = Tensor(0.1, ts.float32) self.array_mul = P.MatMul() self.reshape = P.Reshape() self.shape = tuple(embedding_shape) self.layernorm = layers.LayerNorm((embedding_size,)) self.dropout = layers.Dropout(1 - dropout_prob) self.gather = P.Gather() self.use_relative_positions = use_relative_positions self.slice = P.StridedSlice() self.full_position_embeddings = Parameter(initializer (TruncatedNormal(initializer_range), [max_position_embeddings, embedding_size]), name='full_position_embeddings')
def __init__(self, in_channels, out_channels, initializer_range=0.02, dropout_prob=0.1, compute_type=ts.float32): super(BertOutput, self).__init__() self.dense = layers.Dense(in_channels, out_channels, weight_init=TruncatedNormal(initializer_range)).to_float(compute_type) self.dropout = layers.Dropout(1 - dropout_prob) self.dropout_prob = dropout_prob self.add = P.Add() self.layernorm = layers.LayerNorm((out_channels,)).to_float(compute_type) self.cast = P.Cast()
def __init__(self, config, is_training, num_labels=2, dropout_prob=0.0, use_one_hot_embeddings=False, assessment_method=""): super(BertCLSModel, self).__init__() if not is_training: config.hidden_dropout_prob = 0.0 config.hidden_probs_dropout_prob = 0.0 self.bert = Bert(config, is_training, use_one_hot_embeddings) self.cast = P.Cast() self.weight_init = TruncatedNormal(config.initializer_range) self.log_softmax = P.LogSoftmax(axis=-1) self.dtype = config.dtype self.num_labels = num_labels self.dense_1 = layers.Dense(config.hidden_size, self.num_labels, weight_init=self.weight_init, has_bias=True).to_float(config.compute_type) self.dropout = layers.Dropout(1 - dropout_prob) self.assessment_method = assessment_method
def __init__(self, config, is_training, num_labels=11, use_crf=False, dropout_prob=0.0, use_one_hot_embeddings=False): super(BertNERModel, self).__init__() if not is_training: config.hidden_dropout_prob = 0.0 config.hidden_probs_dropout_prob = 0.0 self.bert = Bert(config, is_training, use_one_hot_embeddings) self.cast = P.Cast() self.weight_init = TruncatedNormal(config.initializer_range) self.log_softmax = P.LogSoftmax(axis=-1) self.dtype = config.dtype self.num_labels = num_labels self.dense_1 = layers.Dense(config.hidden_size, self.num_labels, weight_init=self.weight_init, has_bias=True).to_float(config.compute_type) self.dropout = layers.Dropout(1 - dropout_prob) self.reshape = P.Reshape() self.shape = (-1, config.hidden_size) self.use_crf = use_crf self.origin_shape = (-1, config.seq_length, self.num_labels)
def __init__(self, vocab_size, embedding_size, embedding_shape, use_one_hot_embeddings=False, initializer_range=0.02): super(EmbeddingLookup, self).__init__() self.vocab_size = vocab_size self.use_one_hot_embeddings = use_one_hot_embeddings self.embedding_table = Parameter(initializer (TruncatedNormal(initializer_range), [vocab_size, embedding_size])) self.expand = P.ExpandDims() self.shape_flat = (-1,) self.gather = P.Gather() self.one_hot = P.OneHot() self.on_value = Tensor(1.0, ts.float32) self.off_value = Tensor(0.0, ts.float32) self.array_mul = P.MatMul() self.reshape = P.Reshape() self.shape = tuple(embedding_shape)
def __init__(self, length, depth, max_relative_position, initializer_range, use_one_hot_embeddings=False): super(RelaPosEmbeddingsGenerator, self).__init__() self.depth = depth self.vocab_size = max_relative_position * 2 + 1 self.use_one_hot_embeddings = use_one_hot_embeddings self.embeddings_table = Parameter( initializer(TruncatedNormal(initializer_range), [self.vocab_size, self.depth])) self.relative_positions_matrix = RelaPosMatrixGenerator(length=length, max_relative_position=max_relative_position) self.reshape = P.Reshape() self.one_hot = layers.OneHot(depth=self.vocab_size) self.shape = P.Shape() self.gather = P.Gather() # index_select self.matmul = P.BatchMatMul()
def __init__(self, config, is_training, use_one_hot_embeddings=False): super(Bert, self).__init__() config = copy.deepcopy(config) if not is_training: config.hidden_dropout_prob = 0.0 config.attention_probs_dropout_prob = 0.0 self.seq_length = config.seq_length self.hidden_size = config.hidden_size self.num_hidden_layers = config.num_hidden_layers self.embedding_size = config.hidden_size self.token_type_ids = None self.last_idx = self.num_hidden_layers - 1 output_embedding_shape = [-1, self.seq_length, self.embedding_size] self.bert_embedding_lookup = layers.Embedding( vocab_size=config.vocab_size, embedding_size=self.embedding_size, use_one_hot=use_one_hot_embeddings, embedding_table=TruncatedNormal(config.initializer_range)) self.bert_embedding_postprocessor = EmbeddingPostprocessor( embedding_size=self.embedding_size, embedding_shape=output_embedding_shape, use_relative_positions=config.use_relative_positions, use_token_type=True, token_type_vocab_size=config.type_vocab_size, use_one_hot_embeddings=use_one_hot_embeddings, initializer_range=0.02, max_position_embeddings=config.max_position_embeddings, dropout_prob=config.hidden_dropout_prob) self.bert_encoder = BertTransformer( hidden_size=self.hidden_size, seq_length=self.seq_length, num_attention_heads=config.num_attention_heads, num_hidden_layers=self.num_hidden_layers, intermediate_size=config.intermediate_size, attention_probs_dropout_prob=config.attention_probs_dropout_prob, use_one_hot_embeddings=use_one_hot_embeddings, initializer_range=config.initializer_range, hidden_dropout_prob=config.hidden_dropout_prob, use_relative_positions=config.use_relative_positions, hidden_act=config.hidden_act, compute_type=config.compute_type, return_all_encoders=True) self.cast = P.Cast() self.dtype = config.dtype self.cast_compute_type = SaturateCast(dst_type=config.compute_type) self.slice = P.StridedSlice() self.squeeze_1 = P.Squeeze(axis=1) self.dense = layers.Dense(self.hidden_size, self.hidden_size, activation="tanh", weight_init=TruncatedNormal(config.initializer_range)).to_float(config.compute_type) self._create_attention_mask_from_input_mask = CreateAttentionMaskFromInputMask(config)
def __init__(self, from_tensor_width, to_tensor_width, from_seq_length, to_seq_length, num_attention_heads=1, size_per_head=512, query_act=None, key_act=None, value_act=None, has_attention_mask=False, attention_probs_dropout_prob=0.0, use_one_hot_embeddings=False, initializer_range=0.02, do_return_2d_tensor=False, use_relative_positions=False, compute_type=ts.float32): super(BertAttention, self).__init__() self.from_seq_length = from_seq_length self.to_seq_length = to_seq_length self.num_attention_heads = num_attention_heads self.size_per_head = size_per_head self.has_attention_mask = has_attention_mask self.use_relative_positions = use_relative_positions self.scores_mul = 1.0 / math.sqrt(float(self.size_per_head)) self.reshape = P.Reshape() self.shape_from_2d = (-1, from_tensor_width) self.shape_to_2d = (-1, to_tensor_width) weight = TruncatedNormal(initializer_range) units = num_attention_heads * size_per_head self.query_layer = layers.Dense(from_tensor_width, units, activation=query_act, weight_init=weight).to_float(compute_type) self.key_layer = layers.Dense(to_tensor_width, units, activation=key_act, weight_init=weight).to_float(compute_type) self.value_layer = layers.Dense(to_tensor_width, units, activation=value_act, weight_init=weight).to_float(compute_type) self.shape_from = (-1, from_seq_length, num_attention_heads, size_per_head) self.shape_to = (-1, to_seq_length, num_attention_heads, size_per_head) self.matmul_trans_b = P.BatchMatMul(transpose_b=True) self.multiply = P.Mul() self.transpose = P.Transpose() self.trans_shape = (0, 2, 1, 3) self.trans_shape_relative = (2, 0, 1, 3) self.trans_shape_position = (1, 2, 0, 3) self.multiply_data = -10000.0 self.matmul = P.BatchMatMul() self.softmax = layers.Softmax() self.dropout = layers.Dropout(1 - attention_probs_dropout_prob) if self.has_attention_mask: self.expand_dims = P.ExpandDims() self.sub = P.Sub() self.add = P.Add() self.cast = P.Cast() self.get_dtype = P.DType() if do_return_2d_tensor: self.shape_return = (-1, num_attention_heads * size_per_head) else: self.shape_return = (-1, from_seq_length, num_attention_heads * size_per_head) self.cast_compute_type = SaturateCast(dst_type=compute_type) if self.use_relative_positions: self._generate_relative_positions_embeddings = \ RelaPosEmbeddingsGenerator(length=to_seq_length, depth=size_per_head, max_relative_position=16, initializer_range=initializer_range, use_one_hot_embeddings=use_one_hot_embeddings)