def __init__(self, config):
super(CreateAttentionMaskFromInputMask, self).__init__()
self.input_mask = None
self.cast = P.Cast()
self.reshape = P.Reshape()
self.shape = (-1, 1, config.seq_length)
def construct(self, input_ids, input_mask, token_type_id):
sequence_output, _, _ = self.bert(input_ids, token_type_id, input_mask)
batch_size, seq_length, hidden_size = P.Shape()(sequence_output)
sequence = P.Reshape()(sequence_output, (-1, hidden_size))
logits = self.dense1(sequence)
logits = P.Cast()(logits, self.dtype)
logits = P.Reshape()(logits, (batch_size, seq_length, self.num_labels))
logits = self.log_softmax(logits)
return logits
def __init__(self, src_type=ts.float32, dst_type=ts.float32):
super(SaturateCast, self).__init__()
np_type = ts.dtype_to_nptype(dst_type)
self.tensor_min_type = float(np.finfo(np_type).min)
self.tensor_max_type = float(np.finfo(np_type).max)
self.min_op = P.Minimum()
self.max_op = P.Maximum()
self.cast = P.Cast()
self.dst_type = dst_type
def __init__(self, is_training=True):
super(CrossEntropyCalculation, self).__init__()
self.onehot = P.OneHot()
self.on_value = Tensor(1.0, ts.float32)
self.off_value = Tensor(0.0, ts.float32)
self.reduce_sum = P.ReduceSum()
self.reduce_mean = P.ReduceMean()
self.reshape = P.Reshape()
self.last_idx = (-1, )
self.neg = P.Neg()
self.cast = P.Cast()
self.is_training = is_training
def __init__(self, length, max_relative_position):
super(RelaPosMatrixGenerator, self).__init__()
self._length = length
self._max_relative_position = max_relative_position
self._min_relative_position = -max_relative_position
self.range_length = -length + 1
self.tile = P.Tile()
self.range_mat = P.Reshape()
self.sub = P.Sub()
self.expanddims = P.ExpandDims()
self.cast = P.Cast()
def __init__(self, learning_rate, end_learning_rate, warmup_steps,
decay_steps, power):
super(BertLearningRate, self).__init__()
self.warmup_flag = False
if warmup_steps > 0:
self.warmup_flag = True
self.warmup_lr = WarmUpLR(learning_rate, warmup_steps)
self.decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate,
decay_steps, power)
self.warmup_steps = ts.array([warmup_steps], dtype=ts.float32)
self.greater = P.Greater()
self.one = ts.array([1.0], dtype=ts.float32)
self.cast = P.Cast()
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,
params,
learning_rate=1e-3,
beta1=0.9,
beta2=0.999,
eps=1e-6,
weight_decay=0.0):
super(AdamWeightDecayForBert, self).__init__(learning_rate, params,
weight_decay)
_check_param_value(beta1, beta2, eps, self.cls_name)
self.beta1 = ts.array([beta1], dtype=ts.float32)
self.beta2 = ts.array([beta2], dtype=ts.float32)
self.eps = ts.array([eps], dtype=ts.float32)
self.moments1 = self.parameters.clone(prefix="adam_m", init='zeros')
self.moments2 = self.parameters.clone(prefix="adam_v", init='zeros')
self.hyper_map = P.HyperMap()
self.op_select = P.Select()
self.op_cast = P.Cast()
self.op_reshape = P.Reshape()
self.op_shape = P.Shape()
def __init__(self, network, optimizer, scale_update_layer=None):
super(BertFinetuneLayer, self).__init__(auto_prefix=False)
self.network = network
self.network.set_grad()
self.weights = optimizer.parameters
self.optimizer = optimizer
self.optimizer.global_step = Parameter(initializer(0., [
1,
]),
name='global_step')
self.grad = P.GradOperation(get_by_list=True, sens_param=True)
self.allreduce = P.AllReduce()
self.grad_reducer = None
self.cast = P.Cast()
self.gpu_target = False
if context.get_context("device_target") == "GPU":
self.gpu_target = True
self.float_status = P.FloatStatus()
self.addn = P.AddN()
self.reshape = P.Reshape()
else:
self.alloc_status = P.NPUAllocFloatStatus()
self.get_status = P.NPUGetFloatStatus()
self.clear_before_grad = P.NPUClearFloatStatus()
self.reduce_sum = P.ReduceSum(keep_dims=False)
self.depend_parameter_use = P.Depend()
self.base = Tensor(1, ts.float32)
self.less_equal = P.LessEqual()
self.hyper_map = P.HyperMap()
self.loss_scale = None
self.loss_scaling_manager = scale_update_layer
if scale_update_layer:
self.loss_scale = Parameter(Tensor(
scale_update_layer.get_loss_scale(), dtype=ts.float32),
name="loss_scale")
def __init__(self, network, optimizer, scale_update_layer=None):
super(BertSquadLayer, self).__init__(auto_prefix=False)
self.network = network
self.network.set_grad()
self.weights = optimizer.parameters
self.optimizer = optimizer
self.grad = P.GradOperation(get_by_list=True, sens_param=True)
self.allreduce = P.AllReduce()
self.grad_reducer = None
self.cast = P.Cast()
self.alloc_status = P.NPUAllocFloatStatus()
self.get_status = P.NPUGetFloatStatus()
self.clear_before_grad = P.NPUClearFloatStatus()
self.reduce_sum = P.ReduceSum(keep_dims=False)
self.depend_parameter_use = P.Depend()
self.base = Tensor(1, ts.float32)
self.less_equal = P.LessEqual()
self.hyper_map = P.HyperMap()
self.loss_scale = None
self.loss_scaling_manager = scale_update_layer
if scale_update_layer:
self.loss_scale = Parameter(Tensor(
scale_update_layer.get_loss_scale(), dtype=ts.float32),
name="loss_scale")
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)
