代码拉取完成,页面将自动刷新
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Bert model."""
import math
import copy
import numpy as np
import mindspore.common.dtype as mstype
import mindspore.nn as nn
import mindspore.ops.functional as F
from mindspore.common.initializer import TruncatedNormal, initializer
from mindspore.ops import operations as P
from mindspore.ops import composite as C
from mindspore.common.tensor import Tensor
from mindspore.common.parameter import Parameter
class BertConfig:
"""
Configuration for `BertModel`.
Args:
seq_length (int): Length of input sequence. Default: 128.
vocab_size (int): The shape of each embedding vector. Default: 32000.
hidden_size (int): Size of the bert encoder layers. Default: 768.
num_hidden_layers (int): Number of hidden layers in the BertTransformer encoder
cell. Default: 12.
num_attention_heads (int): Number of attention heads in the BertTransformer
encoder cell. Default: 12.
intermediate_size (int): Size of intermediate layer in the BertTransformer
encoder cell. Default: 3072.
hidden_act (str): Activation function used in the BertTransformer encoder
cell. Default: "gelu".
hidden_dropout_prob (float): The dropout probability for BertOutput. Default: 0.1.
attention_probs_dropout_prob (float): The dropout probability for
BertAttention. Default: 0.1.
max_position_embeddings (int): Maximum length of sequences used in this
model. Default: 512.
type_vocab_size (int): Size of token type vocab. Default: 16.
initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
use_relative_positions (bool): Specifies whether to use relative positions. Default: False.
dtype (:class:`mindspore.dtype`): Data type of the input. Default: mstype.float32.
compute_type (:class:`mindspore.dtype`): Compute type in BertTransformer. Default: mstype.float32.
"""
def __init__(self,
seq_length=128,
vocab_size=32000,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
initializer_range=0.02,
use_relative_positions=False,
dtype=mstype.float32,
compute_type=mstype.float32):
self.seq_length = seq_length
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.use_relative_positions = use_relative_positions
self.dtype = dtype
self.compute_type = compute_type
class EmbeddingLookup(nn.Cell):
"""
A embeddings lookup table with a fixed dictionary and size.
Args:
vocab_size (int): Size of the dictionary of embeddings.
embedding_size (int): The size of each embedding vector.
embedding_shape (list): [batch_size, seq_length, embedding_size], the shape of
each embedding vector.
use_one_hot_embeddings (bool): Specifies whether to use one hot encoding form. Default: False.
initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
"""
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, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.array_mul = P.MatMul()
self.reshape = P.Reshape()
self.shape = tuple(embedding_shape)
def construct(self, input_ids):
"""Get output and embeddings lookup table"""
extended_ids = self.expand(input_ids, -1)
flat_ids = self.reshape(extended_ids, self.shape_flat)
if self.use_one_hot_embeddings:
one_hot_ids = self.one_hot(flat_ids, self.vocab_size, self.on_value, self.off_value)
output_for_reshape = self.array_mul(
one_hot_ids, self.embedding_table)
else:
output_for_reshape = self.gather(self.embedding_table, flat_ids, 0)
output = self.reshape(output_for_reshape, self.shape)
return output, self.embedding_table
class EmbeddingPostprocessor(nn.Cell):
"""
Postprocessors apply positional and token type embeddings to word embeddings.
Args:
embedding_size (int): The size of each embedding vector.
embedding_shape (list): [batch_size, seq_length, embedding_size], the shape of
each embedding vector.
use_token_type (bool): Specifies whether to use token type embeddings. Default: False.
token_type_vocab_size (int): Size of token type vocab. Default: 16.
use_one_hot_embeddings (bool): Specifies whether to use one hot encoding form. Default: False.
initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
max_position_embeddings (int): Maximum length of sequences used in this
model. Default: 512.
dropout_prob (float): The dropout probability. Default: 0.1.
"""
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.token_type_embedding = nn.Embedding(
vocab_size=token_type_vocab_size,
embedding_size=embedding_size,
use_one_hot=use_one_hot_embeddings)
self.shape_flat = (-1,)
self.one_hot = P.OneHot()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.1, mstype.float32)
self.array_mul = P.MatMul()
self.reshape = P.Reshape()
self.shape = tuple(embedding_shape)
self.dropout = nn.Dropout(1 - dropout_prob)
self.gather = P.Gather()
self.use_relative_positions = use_relative_positions
self.slice = P.StridedSlice()
_, seq, _ = self.shape
self.full_position_embedding = nn.Embedding(
vocab_size=max_position_embeddings,
embedding_size=embedding_size,
use_one_hot=False)
self.layernorm = nn.LayerNorm((embedding_size,))
self.position_ids = Tensor(np.arange(seq).reshape(-1, seq).astype(np.int32))
self.add = P.Add()
def construct(self, token_type_ids, word_embeddings):
"""Postprocessors apply positional and token type embeddings to word embeddings."""
output = word_embeddings
if self.use_token_type:
token_type_embeddings = self.token_type_embedding(token_type_ids)
output = self.add(output, token_type_embeddings)
if not self.use_relative_positions:
shape = F.shape(output)
position_ids = self.position_ids[:, :shape[1]]
position_embeddings = self.full_position_embedding(position_ids)
output = self.add(output, position_embeddings)
output = self.layernorm(output)
output = self.dropout(output)
return output
class BertOutput(nn.Cell):
"""
Apply a linear computation to hidden status and a residual computation to input.
Args:
in_channels (int): Input channels.
out_channels (int): Output channels.
initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
dropout_prob (float): The dropout probability. Default: 0.1.
compute_type (:class:`mindspore.dtype`): Compute type in BertTransformer. Default: mstype.float32.
"""
def __init__(self,
in_channels,
out_channels,
initializer_range=0.02,
dropout_prob=0.1,
compute_type=mstype.float32):
super(BertOutput, self).__init__()
self.dense = nn.Dense(in_channels, out_channels,
weight_init=TruncatedNormal(initializer_range)).to_float(compute_type)
self.dropout = nn.Dropout(1 - dropout_prob)
self.dropout_prob = dropout_prob
self.add = P.Add()
self.layernorm = nn.LayerNorm((out_channels,)).to_float(compute_type)
self.cast = P.Cast()
def construct(self, hidden_status, input_tensor):
output = self.dense(hidden_status)
output = self.dropout(output)
output = self.add(input_tensor, output)
output = self.layernorm(output)
return output
class RelaPosMatrixGenerator(nn.Cell):
"""
Generates matrix of relative positions between inputs.
Args:
length (int): Length of one dim for the matrix to be generated.
max_relative_position (int): Max value of relative position.
"""
def __init__(self, max_relative_position):
super(RelaPosMatrixGenerator, self).__init__()
self._max_relative_position = max_relative_position
self._min_relative_position = -max_relative_position
self.tile = P.Tile()
self.range_mat = P.Reshape()
self.sub = P.Sub()
self.expanddims = P.ExpandDims()
self.cast = P.Cast()
def construct(self, length):
"""Generates matrix of relative positions between inputs."""
range_vec_row_out = self.cast(F.tuple_to_array(F.make_range(length)), mstype.int32)
range_vec_col_out = self.range_mat(range_vec_row_out, (length, -1))
tile_row_out = self.tile(range_vec_row_out, (length,))
tile_col_out = self.tile(range_vec_col_out, (1, length))
range_mat_out = self.range_mat(tile_row_out, (length, length))
transpose_out = self.range_mat(tile_col_out, (length, length))
distance_mat = self.sub(range_mat_out, transpose_out)
distance_mat_clipped = C.clip_by_value(distance_mat,
self._min_relative_position,
self._max_relative_position)
# Shift values to be >=0. Each integer still uniquely identifies a
# relative position difference.
final_mat = distance_mat_clipped + self._max_relative_position
return final_mat
class RelaPosEmbeddingsGenerator(nn.Cell):
"""
Generates tensor of size [length, length, depth].
Args:
length (int): Length of one dim for the matrix to be generated.
depth (int): Size of each attention head.
max_relative_position (int): Maxmum value of relative position.
initializer_range (float): Initialization value of TruncatedNormal.
use_one_hot_embeddings (bool): Specifies whether to use one hot encoding form. Default: False.
"""
def __init__(self,
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(max_relative_position=max_relative_position)
self.reshape = P.Reshape()
self.one_hot = nn.OneHot(depth=self.vocab_size)
self.shape = P.Shape()
self.gather = P.Gather() # index_select
self.matmul = P.BatchMatMul()
def construct(self, length):
"""Generate embedding for each relative position of dimension depth."""
relative_positions_matrix_out = self.relative_positions_matrix(length)
if self.use_one_hot_embeddings:
flat_relative_positions_matrix = self.reshape(relative_positions_matrix_out, (-1,))
one_hot_relative_positions_matrix = self.one_hot(
flat_relative_positions_matrix)
embeddings = self.matmul(one_hot_relative_positions_matrix, self.embeddings_table)
my_shape = self.shape(relative_positions_matrix_out) + (self.depth,)
embeddings = self.reshape(embeddings, my_shape)
else:
embeddings = self.gather(self.embeddings_table,
relative_positions_matrix_out, 0)
return embeddings
class SaturateCast(nn.Cell):
"""
Performs a safe saturating cast. This operation applies proper clamping before casting to prevent
the danger that the value will overflow or underflow.
Args:
src_type (:class:`mindspore.dtype`): The type of the elements of the input tensor. Default: mstype.float32.
dst_type (:class:`mindspore.dtype`): The type of the elements of the output tensor. Default: mstype.float32.
"""
def __init__(self, src_type=mstype.float32, dst_type=mstype.float32):
super(SaturateCast, self).__init__()
np_type = mstype.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 construct(self, x):
out = self.max_op(x, self.tensor_min_type)
out = self.min_op(out, self.tensor_max_type)
return self.cast(out, self.dst_type)
class BertAttention(nn.Cell):
"""
Apply multi-headed attention from "from_tensor" to "to_tensor".
Args:
from_tensor_width (int): Size of last dim of from_tensor.
to_tensor_width (int): Size of last dim of to_tensor.
num_attention_heads (int): Number of attention heads. Default: 1.
size_per_head (int): Size of each attention head. Default: 512.
query_act (str): Activation function for the query transform. Default: None.
key_act (str): Activation function for the key transform. Default: None.
value_act (str): Activation function for the value transform. Default: None.
has_attention_mask (bool): Specifies whether to use attention mask. Default: False.
attention_probs_dropout_prob (float): The dropout probability for
BertAttention. Default: 0.0.
use_one_hot_embeddings (bool): Specifies whether to use one hot encoding form. Default: False.
initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
use_relative_positions (bool): Specifies whether to use relative positions. Default: False.
compute_type (:class:`mindspore.dtype`): Compute type in BertAttention. Default: mstype.float32.
"""
def __init__(self,
from_tensor_width,
to_tensor_width,
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,
use_relative_positions=False,
compute_type=mstype.float32):
super(BertAttention, self).__init__()
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 = nn.Dense(from_tensor_width,
units,
activation=query_act,
weight_init=weight).to_float(compute_type)
self.key_layer = nn.Dense(to_tensor_width,
units,
activation=key_act,
weight_init=weight).to_float(compute_type)
self.value_layer = nn.Dense(to_tensor_width,
units,
activation=value_act,
weight_init=weight).to_float(compute_type)
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 = nn.Softmax()
self.dropout = nn.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()
self.shape_return = (-1, 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(depth=size_per_head,
max_relative_position=16,
initializer_range=initializer_range,
use_one_hot_embeddings=use_one_hot_embeddings)
def construct(self, from_tensor, to_tensor, attention_mask):
"""reshape 2d/3d input tensors to 2d"""
shape_from = F.shape(attention_mask)[2]
from_tensor = F.depend(from_tensor, shape_from)
from_tensor_2d = self.reshape(from_tensor, self.shape_from_2d)
to_tensor_2d = self.reshape(to_tensor, self.shape_to_2d)
query_out = self.query_layer(from_tensor_2d)
key_out = self.key_layer(to_tensor_2d)
value_out = self.value_layer(to_tensor_2d)
query_layer = self.reshape(query_out, (-1, shape_from, self.num_attention_heads, self.size_per_head))
query_layer = self.transpose(query_layer, self.trans_shape)
key_layer = self.reshape(key_out, (-1, shape_from, self.num_attention_heads, self.size_per_head))
key_layer = self.transpose(key_layer, self.trans_shape)
attention_scores = self.matmul_trans_b(query_layer, key_layer)
# use_relative_position, supplementary logic
if self.use_relative_positions:
# relations_keys is [F|T, F|T, H]
relations_keys = self._generate_relative_positions_embeddings(shape_from)
relations_keys = self.cast_compute_type(relations_keys)
# query_layer_t is [F, B, N, H]
query_layer_t = self.transpose(query_layer, self.trans_shape_relative)
# query_layer_r is [F, B * N, H]
query_layer_r = self.reshape(query_layer_t,
(shape_from,
-1,
self.size_per_head))
# key_position_scores is [F, B * N, F|T]
key_position_scores = self.matmul_trans_b(query_layer_r,
relations_keys)
# key_position_scores_r is [F, B, N, F|T]
key_position_scores_r = self.reshape(key_position_scores,
(shape_from,
-1,
self.num_attention_heads,
shape_from))
# key_position_scores_r_t is [B, N, F, F|T]
key_position_scores_r_t = self.transpose(key_position_scores_r,
self.trans_shape_position)
attention_scores = attention_scores + key_position_scores_r_t
attention_scores = self.multiply(self.scores_mul, attention_scores)
if self.has_attention_mask:
attention_mask = self.expand_dims(attention_mask, 1)
multiply_out = self.sub(self.cast(F.tuple_to_array((1.0,)), self.get_dtype(attention_scores)),
self.cast(attention_mask, self.get_dtype(attention_scores)))
adder = self.multiply(multiply_out, self.multiply_data)
attention_scores = self.add(adder, attention_scores)
attention_probs = self.softmax(attention_scores)
attention_probs = self.dropout(attention_probs)
value_layer = self.reshape(value_out, (-1, shape_from, self.num_attention_heads, self.size_per_head))
value_layer = self.transpose(value_layer, self.trans_shape)
context_layer = self.matmul(attention_probs, value_layer)
# use_relative_position, supplementary logic
if self.use_relative_positions:
# relations_values is [F|T, F|T, H]
relations_values = self._generate_relative_positions_embeddings(shape_from)
relations_values = self.cast_compute_type(relations_values)
# attention_probs_t is [F, B, N, T]
attention_probs_t = self.transpose(attention_probs, self.trans_shape_relative)
# attention_probs_r is [F, B * N, T]
attention_probs_r = self.reshape(
attention_probs_t,
(shape_from,
-1,
shape_from))
# value_position_scores is [F, B * N, H]
value_position_scores = self.matmul(attention_probs_r,
relations_values)
# value_position_scores_r is [F, B, N, H]
value_position_scores_r = self.reshape(value_position_scores,
(shape_from,
-1,
self.num_attention_heads,
self.size_per_head))
# value_position_scores_r_t is [B, N, F, H]
value_position_scores_r_t = self.transpose(value_position_scores_r,
self.trans_shape_position)
context_layer = context_layer + value_position_scores_r_t
context_layer = self.transpose(context_layer, self.trans_shape)
context_layer = self.reshape(context_layer, self.shape_return)
return context_layer
class BertSelfAttention(nn.Cell):
"""
Apply self-attention.
Args:
hidden_size (int): Size of the bert encoder layers.
num_attention_heads (int): Number of attention heads. Default: 12.
attention_probs_dropout_prob (float): The dropout probability for
BertAttention. Default: 0.1.
use_one_hot_embeddings (bool): Specifies whether to use one_hot encoding form. Default: False.
initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
hidden_dropout_prob (float): The dropout probability for BertOutput. Default: 0.1.
use_relative_positions (bool): Specifies whether to use relative positions. Default: False.
compute_type (:class:`mindspore.dtype`): Compute type in BertSelfAttention. Default: mstype.float32.
"""
def __init__(self,
hidden_size,
num_attention_heads=12,
attention_probs_dropout_prob=0.1,
use_one_hot_embeddings=False,
initializer_range=0.02,
hidden_dropout_prob=0.1,
use_relative_positions=False,
compute_type=mstype.float32):
super(BertSelfAttention, self).__init__()
if hidden_size % num_attention_heads != 0:
raise ValueError("The hidden size (%d) is not a multiple of the number "
"of attention heads (%d)" % (hidden_size, num_attention_heads))
self.size_per_head = int(hidden_size / num_attention_heads)
self.attention = BertAttention(
from_tensor_width=hidden_size,
to_tensor_width=hidden_size,
num_attention_heads=num_attention_heads,
size_per_head=self.size_per_head,
attention_probs_dropout_prob=attention_probs_dropout_prob,
use_one_hot_embeddings=use_one_hot_embeddings,
initializer_range=initializer_range,
use_relative_positions=use_relative_positions,
has_attention_mask=True,
compute_type=compute_type)
self.output = BertOutput(in_channels=hidden_size,
out_channels=hidden_size,
initializer_range=initializer_range,
dropout_prob=hidden_dropout_prob,
compute_type=compute_type)
self.reshape = P.Reshape()
self.shape = (-1, hidden_size)
def construct(self, input_tensor, attention_mask):
attention_output = self.attention(input_tensor, input_tensor, attention_mask)
output = self.output(attention_output, input_tensor)
return output
class BertEncoderCell(nn.Cell):
"""
Encoder cells used in BertTransformer.
Args:
hidden_size (int): Size of the bert encoder layers. Default: 768.
num_attention_heads (int): Number of attention heads. Default: 12.
intermediate_size (int): Size of intermediate layer. Default: 3072.
attention_probs_dropout_prob (float): The dropout probability for
BertAttention. Default: 0.02.
use_one_hot_embeddings (bool): Specifies whether to use one hot encoding form. Default: False.
initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
hidden_dropout_prob (float): The dropout probability for BertOutput. Default: 0.1.
use_relative_positions (bool): Specifies whether to use relative positions. Default: False.
hidden_act (str): Activation function. Default: "gelu".
compute_type (:class:`mindspore.dtype`): Compute type in attention. Default: mstype.float32.
"""
def __init__(self,
hidden_size=768,
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=mstype.float32):
super(BertEncoderCell, self).__init__()
self.attention = BertSelfAttention(
hidden_size=hidden_size,
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 = nn.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 construct(self, hidden_states, attention_mask):
# self-attention
attention_output = self.attention(hidden_states, attention_mask)
# feed construct
intermediate_output = self.intermediate(attention_output)
# add and normalize
output = self.output(intermediate_output, attention_output)
return output
class BertTransformer(nn.Cell):
"""
Multi-layer bert transformer.
Args:
hidden_size (int): Size of the encoder layers.
num_hidden_layers (int): Number of hidden layers in encoder cells.
num_attention_heads (int): Number of attention heads in encoder cells. Default: 12.
intermediate_size (int): Size of intermediate layer in encoder cells. Default: 3072.
attention_probs_dropout_prob (float): The dropout probability for
BertAttention. Default: 0.1.
use_one_hot_embeddings (bool): Specifies whether to use one hot encoding form. Default: False.
initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
hidden_dropout_prob (float): The dropout probability for BertOutput. Default: 0.1.
use_relative_positions (bool): Specifies whether to use relative positions. Default: False.
hidden_act (str): Activation function used in the encoder cells. Default: "gelu".
compute_type (:class:`mindspore.dtype`): Compute type in BertTransformer. Default: mstype.float32.
return_all_encoders (bool): Specifies whether to return all encoders. Default: False.
"""
def __init__(self,
hidden_size,
num_hidden_layers,
num_attention_heads=12,
intermediate_size=3072,
attention_probs_dropout_prob=0.1,
use_one_hot_embeddings=False,
initializer_range=0.02,
hidden_dropout_prob=0.1,
use_relative_positions=False,
hidden_act="gelu",
compute_type=mstype.float32,
return_all_encoders=False):
super(BertTransformer, self).__init__()
self.return_all_encoders = return_all_encoders
layers = []
for _ in range(num_hidden_layers):
layer = BertEncoderCell(hidden_size=hidden_size,
num_attention_heads=num_attention_heads,
intermediate_size=intermediate_size,
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,
hidden_act=hidden_act,
compute_type=compute_type)
layers.append(layer)
self.layers = nn.CellList(layers)
self.reshape = P.Reshape()
self.shape = (-1, hidden_size)
def construct(self, input_tensor, attention_mask):
"""Multi-layer bert transformer."""
prev_output = self.reshape(input_tensor, self.shape)
all_encoder_layers = ()
for layer_module in self.layers:
layer_output = layer_module(prev_output, attention_mask)
prev_output = layer_output
if self.return_all_encoders:
shape = F.shape(input_tensor)
layer_output = self.reshape(layer_output, shape)
all_encoder_layers = all_encoder_layers + (layer_output,)
if not self.return_all_encoders:
shape = F.shape(input_tensor)
prev_output = self.reshape(prev_output, shape)
all_encoder_layers = all_encoder_layers + (prev_output,)
return all_encoder_layers
class CreateAttentionMaskFromInputMask(nn.Cell):
"""
Create attention mask according to input mask.
Args:
config (Class): Configuration for BertModel.
"""
def __init__(self, config):
super(CreateAttentionMaskFromInputMask, self).__init__()
self.input_mask = None
self.cast = P.Cast()
self.reshape = P.Reshape()
def construct(self, input_mask):
seq_length = F.shape(input_mask)[1]
attention_mask = self.cast(self.reshape(input_mask, (-1, 1, seq_length)), mstype.float32)
return attention_mask
class BertModel(nn.Cell):
"""
Bidirectional Encoder Representations from Transformers.
Args:
config (Class): Configuration for BertModel.
is_training (bool): True for training mode. False for eval mode.
use_one_hot_embeddings (bool): Specifies whether to use one hot encoding form. Default: False.
"""
def __init__(self,
config,
is_training,
use_one_hot_embeddings=False):
super(BertModel, self).__init__()
config = copy.deepcopy(config)
if not is_training:
config.hidden_dropout_prob = 0.0
config.attention_probs_dropout_prob = 0.0
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, config.seq_length, self.embedding_size]
self.bert_embedding_lookup = nn.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,
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 = nn.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 construct(self, input_ids, token_type_ids, input_mask):
"""Bidirectional Encoder Representations from Transformers."""
# embedding
embedding_tables = self.bert_embedding_lookup.embedding_table
word_embeddings = self.bert_embedding_lookup(input_ids)
embedding_output = self.bert_embedding_postprocessor(token_type_ids,
word_embeddings)
# attention mask [batch_size, seq_length, seq_length]
attention_mask = self._create_attention_mask_from_input_mask(input_mask)
# bert encoder
encoder_output = self.bert_encoder(self.cast_compute_type(embedding_output),
attention_mask)
sequence_output = self.cast(encoder_output[self.last_idx], self.dtype)
# pooler
batch_size = P.Shape()(input_ids)[0]
sequence_slice = self.slice(sequence_output,
(0, 0, 0),
(batch_size, 1, self.hidden_size),
(1, 1, 1))
first_token = self.squeeze_1(sequence_slice)
pooled_output = self.dense(first_token)
pooled_output = self.cast(pooled_output, self.dtype)
return sequence_output, pooled_output, embedding_tables
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