[人工智能]基于Tensorflow实现Transformer模型

1.Transformer模型

import tensorflow as tf  
from official.transformer.model import attention_layer
from official.transformer.model import beam_search
from official.transformer.model import embedding_layer
from official.transformer.model import ffn_layer
from official.transformer.model import model_utils
from official.transformer.utils.tokenizer import EOS_ID

class Transformer(object):
    """
    transformer模型由encoder和decoder创建。输入是int序列，encoder产生连续输出，decoder使用ecoder output输出序列概率
    """
    def __int__(self,params,train):
        """
        transformer model 初始化
        :param params: 超参数设置，如：layer size,dropout rate 等
        :param train: train模式使用dropput
        :return:
        """

        self.trian=train
        self.params=params

        # 创建embedding层，input/output embedding,positional embedding
        # matmul在tpu上训练速度更快，gather在cpu，gpu更快
        self.embedding_softmax_layer=embedding_layer.EmbeddingSharedWeights(
            params['vocab_size'],params['hidden_size'],
            method='matmul' if params['tpu'] else 'gather'
        )
    def __call__(self, inputs, targets=None):
        """
        训练/预测阶段的模型输出
        :param input: tensor shape[batch_size,input_lenght]
        :param targets: None 或者 shape[batch_size,target_length]
        :return: 训练模式下，输出[batch_size,target_length,vocab_size]；预测模式下，输出字典:
        {
        output:[batch_size,decoded_length]
        # BLEU分数
        score:[batch_size,float]
        }
        """
        # 使用方差缩放
        initizlizer=tf.variance_scaling_initializer(
            self.params['initializerz_gain'],mode='fan_avg',
            distribution='unform'
        )

        with tf.variable_scope('transformer',initializer=initizlizer):
            # 计算encoder，decoder中的attention bias
            attention_bias=model_utils.get_padding_bias(inputs)

            # 获取encoder output
            encoder_outputs=self.encode(inputs,attention_bias)

            # 训练模式，预测模式不同输出
            if targets == None:
                return self.predict(encoder_outputs,attention_bias)
            else:
                logits=self.decode(targets,encoder_outputs,attention_bias)
                return logits
    def encode(self,inputs,attention_bias):
        """
        :param inputs: int shape[batch_size,input_length]
        :param attention_bias:float shape[batch_size,1,1,input_length]
        :return: float shape[batch_size,input_length,hidden_size]
        """
        with tf.name_scope('encode'):
            # encode_input 由 input embedding,positional encoding 合并创建，并添加dropout
            # 此时应注意 input embedding,positional encoding 的维度大小，可以两者相加

            embedding_inputs=self.embedding_softmax_layer(inputs)
            inputs_padding=model_utils.get_padding(inputs)

            with tf.name_scope('add_pos_encoding'):
                lenth=tf.shape(embedding_inputs)[1]
                pos_encoding=model_utils.get_position_encoding(
                    lenth,self.params['hidden_size']
                )
                encoder_inputs=embedding_inputs+pos_encoding

            # 训练模式使用dropout
            if self.train:
                encoder_inputs=tf.nn.dropout(
                    encoder_inputs,1-self.params['layer_postprocess_dropout']
                )

                # encode,decode 都是默认6层
                return self.encode_stack(encoder_inputs,attention_bias,inputs_padding)

    def decode(self,targets,encoder_outputs,attention_bias):
        """

        :param targets: int shape[batch_size,target_size]
        :param encoder_outputs:  float shape[batch_size,input_lenth,hidden_size]
        :param attention_bias: float shape[batch_size,1,1,input_length]
        :return: float shape[batch_size,target_lenth,vocab_size]
        """
        with tf.name_scope('decode'):
            # 将decode input向右移一位（需要把decoder的输入前面加上开始符号并去掉最后一位。然后最终预测出完整的targets）
            # 并添加 positional encoding使用dropout
            decoder_inputs=self.embedding_softmax_layer(targets)

            # 向右移一位，并去除最后一位
            with tf.name_scope('shift_targets'):
                decoder_inputs=tf.pad(
                    decoder_inputs,[[0,0],[1,0],[0,0]]
                )[:,:-1,:]

            # 添加pos_encoding
            with tf.name_scope('add_pos_encoding'):
                length=tf.shape(decoder_inputs)[1]
                decoder_inputs+=model_utils.get_position_encoding(
                    length,self.params['hidden_size']
                )

            # 训练模式使用dropout
            if self.train:
                decoder_inputs=tf.nn.dropout(
                    decoder_inputs,1-self.params['layer_posprocess_dropout']
                )

            # 多头注意力层
            decoder_self_attention_bias=model_utils.get_decoder_self_attention_bias(length)

            outputs=self.decoder_stack(
                decoder_inputs,encoder_outputs,decoder_self_attention_bias,attention_bias
            )

            logits=self.embedding_softmax_layer.linear(outputs)

            return logits
    def _get_symbols_to_logits_fn(self,max_decode_length):
        """
        返回一个用于计算下一个tokens模型输出值的方法
        :param max_decode_length:
        :return:
        """
        timing_signal=model_utils.get_position_encoding(
            max_decode_length+1,self.params['hidden_size']
        )
        decoder_self_atttention_bias=model_utils.get_decoder_self_attention_bias(max_decode_length)

        def symbols_to_logits_fn(ids,i,cache):
            """
            生成下一个模型输出值ID
            :param ids:当前编码序列
            :param i: 循环索引
            :param cache: 保存ecoder_output,encoder-decoder attention bias,上一个decoder attention bias值
            :return: ([batch_size*beam_size,vocab_size],updated cache values)
            """

            # 将decode input 设置为最后一个输出ID
            decoder_input=ids[:,-1,:]

            # decode input 通过embedding并添加timing signal
            decoder_input=self.embedding_softmax_layer(decoder_input)
            decoder_input+=timing_signal[i:i+1]

            self_attention_bias=decoder_self_atttention_bias[:, :, i:i + 1, :i + 1]
            decoder_outputs=self.decoder_stack(
                decoder_input,cache.get('encoder_outputs'),self_attention_bias,
                cache.get('encoder_decoder_attention_bias'),cache
            )

            # 模型最后是一层全连接层+softmax层
            logits=self.embedding_softmax_layer.linear(decoder_outputs)
            logits=tf.squeeze(logits,axis=[1])

            return logits,cache
        return symbols_to_logits_fn

    def predict(self,encoder_outputs,encoder_decoder_attention_bias):
        """

        :param endoer_outputs:
        :param encoder_decoder_attention_bias:
        :return:
        """
        # encoder_outputs shape[batch_size,input_length,hidden_size]
        batch_size=tf.shape(encoder_outputs)[0]
        input_length=tf.shape(encoder_outputs)[1]
        max_decode_length=input_length+self.params['extra_decode_length']
        symbols_to_logits_fn=self._get_symbols_to_logits_fn(max_decode_length)

        # 初始化sybols_to_logits_fn ID输入
        initial_ids=tf.zeros(shape=[batch_size],dtype=tf.int32)

        # 保存每一层的decode attention值
        cache={
            'layer_%d'%layer:{
                'k':tf.zeros([batch_size,0,self.params['hidden_size']]),
                'v':tf.zeros([batch_size,0,self.params['hidden_size']])
            }for layer in range(self.params['num_hidden_layers'])
        }

        cache['encoder_outputs']=encoder_outputs
        cache['encoder_decoder_attention_bias']=encoder_decoder_attention_bias

        # 使用beam search搜索
        decoded_ids,scores=beam_search.sequence_beam_search(
            symbols_to_logits_fn=symbols_to_logits_fn,
            initial_ids=initial_ids,
            initial_cache=cache,
            vocab_size=self.params["vocab_size"],
            beam_size=self.params["beam_size"],
            alpha=self.params["alpha"],
            max_decode_length=max_decode_length,
            eos_id=EOS_ID
        )

        #获取每个batch数据中，顶部数据
        top_decoded_ids=decoded_ids[:,0,1:]
        top_scores=scores[:,0]

        return {'outputs':top_decoded_ids,"scores":top_scores}
class LayerNormalization(tf.keras.layers.Layer):
    # 层归一化
    def __int__(self,hidden_size):
        super(LayerNormalization,self).__init__()
        self.hidden_size=hidden_size
    def build(self,_):
        self.scale=tf.get_variable('layer_nor_scale',[self.hidden_size],initializer=tf.ones_initializer())
        self.bias=tf.get_variable('layer_norm_bias',[self.hidden_size],initializer=tf.zeros_initializer())
        self.built=True

    def call(self, x, epsilon=1e-6):
        mean = tf.reduce_mean(x, axis=[-1], keepdims=True)
        variance = tf.reduce_mean(tf.square(x - mean), axis=[-1], keepdims=True)
        norm_x = (x - mean) * tf.rsqrt(variance + epsilon)
        return norm_x * self.scale + self.bias
class PrePostProcessingWrapper(object):
    """
    用于包装模型起点的attention层和最后的feed_forward全连接层
    """
    def __int__(self,layer,params,train):
        self.layer=layer,
        # 每层都使用到dropout
        self.postprocess_dropout=params['layer_postprocess_dropout']
        self.train=train
        self.layer_norm=LayerNormalization(params['hidden_size'])
    def __call__(self, x,*args, **kwargs):
        # 层归一化
        y=self.layer_norm(x)

        y=self.layer(y,*args,**kwargs)

        # 训练模式使用dropout
        # 应用残差网络

        if self.train:
            y=tf.nn.dropout(y,1-self.postprocess_dropout)
        return x+y
class EncoderStack(tf.keras.layers.Layer):
    """
    模型默认6层encoder,每一层有两个子层：1，self-attention层，2，feedforward全连接层（此层内又有两个子层）
    """
    def __init__(self,params,train):
        super(EncoderStack,self).__init__()
        self.layers=[]

        for _ in range(params['num_hidden_layers']):
            #创建子层
            #多头注意力模型默认是8个
            self_attention_layer=attention_layer.SelfAttention(
                params['hidden_size'],params['num_heads'],
                params['attention_dropout'],train
            )
            feed_forward_network=ffn_layer.FeedFowardNetwork(
                params['hidden_size'],params['filter_size'],
                params['relu_dropout'],train,params['allow_ffn_add']
            )

            self.layers.append([
                PrePostProcessingWrapper(self_attention_layer,params,train),
                PrePostProcessingWrapper(feed_forward_network,params,train)
            ])

            # 创建最后一层，层归一化
            self.output_normalization=LayerNormalization(params['hidden_size'])

    def call(self,encoder_inputs,attention_bias,inputs_padding):
        """
        返回叠层的encoder output
        :param encoder_inputs: int shape[batch_size,input_length,hidden_size]
        :param attention_bias: shape[batch_size,1,1,input_length]
        :param inputs_padding:
        :return: float shape[batch_size,input_length,hidden_size]
        """
        for n,layer in enumerate(self.layers):
            self_attention_layer=layer[0]
            feed_forward_network=layer[1]

            with tf.variable_scope('layer_%d'%n):
                with tf.variable_scope('self_attention'):
                    encoder_inputs=self_attention_layer(encoder_inputs,attention_bias)
                with tf.variable_scope('ffn'):
                    encoder_inputs=feed_forward_network(encoder_inputs,inputs_padding)
        return self.output_normalization

class DecoderStack(tf.keras.layers.Layer):
    """
    层数与encoder一样，区别是decoder有三层
    1，attention层
    2，融合encoder output 前一个attention层的多头注意力层
    3，feedforward全连接层（此层内又有两个子层）
    """
    def __int__(self,params,train):
        super(DecoderStack,self).__init__()
        self.layers=[]

        for _ in range(params['num_hidden_size']):

            # attention层
            self_attention_layer=attention_layer.SelfAttention(
                params['hidden_size'],params['num_heads'],
                params['attention_dropout'],train
            )
            # 融合encoder output 前一个attention层的多头注意力层
            enc_dec_attention_layer=attention_layer.Attention(
                params['hidden_size'],params['num_heads'],
                params['attention_dropout'],train
            )
            # feedforward全连接层（此层内又有两个子层）
            feed_forward_network=ffn_layer.FeedFowardNetwork(
                params['hidden_size'],params['filter_size'],
                params['relu_dropout'],train,params['allow_ffn_pad']
            )

            self.layers.append([
                PrePostProcessingWrapper(self_attention_layer,params,train),
                PrePostProcessingWrapper(enc_dec_attention_layer,params,train),
                PrePostProcessingWrapper(feed_forward_network,params,train)
            ])

        # 最后，添加层归一化
        self.output_normalization=LayerNormalization(params['hidden_size'])
    def call(self,decoder_inputs, encoder_outputs, decoder_self_attention_bias,
           attention_bias, cache=None):
        """

        :param decoder_inputs: shape[batch_size,target_length,hidden_size]
        :param encoder_outputs: shape[batch_size,input_length,hidden_size]
        :param decoder_self_attention_bias:[1,1,target_len,target_length]
        :param attention_bias: shape[batch_size,1,1,input_length]
        :param cache:
        :return: float shape[batch_size,target_length,hidden_size]
        """

        for n,layer in enumerate(self.layers):

            # 分别是decoder的三层
            self_attention_layer=layer[0]
            enc_dec_attention_layer=layer[1]
            feed_forward_network=layer[2]

            layer_name = "layer_%d" % n
            layer_cache = cache[layer_name] if cache is not None else None
            
            # 将input送入模型
            with tf.variable_scope(layer_name):
                with tf.variable_scope('self_attention'):
                    decoder_inputs=self_attention_layer(
                        decoder_inputs,decoder_self_attention_bias,cache
                    )
                    with tf.variable_scope('encdec_attention'):
                        decoder_inputs=enc_dec_attention_layer(
                            decoder_inputs,encoder_outputs,attention_bias
                        )
                    with tf.variable_scope('ffn'):
                        decoder_inputs=feed_forward_network(decoder_inputs)
        # 最后进行层归一化
        return self.output_normalization(decoder_inputs)

2.Attention

import tensorflow as tf

class Attention(tf.keras.layers.Layer):
    """
    多头注意力层
    """
    def __init__(self,hidden_size,num_heads,attention_dropout,train):
        # hidden 必须能与 num_head 整除
        if hidden_size % num_heads != 0:
            raise ValueError('Hidden size must be evenly divisible by the number of ""heads')
        super(Attention,self).__init__()
        self.hidden_size=hidden_size
        self.num_heads=num_heads
        self.attention_dropout=attention_dropout
        self.train=train

        # 计算'q','k','v'
        self.q_dense_layer=tf.keras.layers.Dense(hidden_size,use_bias=False,name='q')
        self.k_dense_layer=tf.keras.layers.Dense(hidden_size,use_bias=False,name='k')
        self.v_dense_layer=tf.keras.layers.Dense(hidden_size,use_bias=False,name='v')

        # attention输出层
        self.output_dense_layer=tf.keras.layers.Dense(hidden_size,use_bias=False,name='outpout_dropout')
    def split_heads(self,x):
        """
        将x拆分不同的注意力head，并将结果转置(转置的目的是为了矩阵相乘时维度正确)
        :param x: shape[batch_size,length,hidden_size]
        :return: shape[batch_size,num_heads,length,hidden_size/num_heads]
        """

        with tf.name_scope('split_heads'):
            batch_size=tf.shape(x)[0]
            length=tf.shape(x)[1]

            # 计算最后一个维度的深度
            depth=(self.hidden_size // self.num_heads)

            # 拆分最后一个维度
            x=tf.reshape(x,[batch_size,length,self.num_heads,depth])

            # 将结果转置,即：[batch_size,self.num_heads,length,depth]
            return tf.transpose(x,[0,2,1,3])

    def combine_heads(self,x):
        """
        将拆分的张量再次连接(split_heads逆操作),input是split_heads_fn的输出
        :param x: shape[batch_size,num_heads,length,hidden_size/num_heads]
        :return:  shape[batch_size,length,hidden_size]
        """
        with tf.name_scope('combine_heads'):
            batchs_size=tf.shape(x)[0]
            length=tf.shape(x)[2]

            # [batch_size,length,num_heads,depth]
            x=tf.transpose(x,[0,2,1,3])
            return tf.reshape(x,[batchs_size,length,self.hidden_size])


    def call(self,x,y,bias,cache=None):
        """

        :param x: shape[batch_size,length_x,hidden_size]
        :param y: shape[batch_size,length_y,hidden_size]
        :param bias: 与点积结果相加
        :param cache: 预测模式使用；返回类型为字典：
        {
        'k':shape[batch_size,i,key_channels],
        'v':shape[batch_size,i,value_channels]
        }
        i:当前decoded长度
        :return: shape[batch_size,length_x,hidden_size]
        """
        # 获取'q','k','v'
        q=self.q_dense_layer(x)
        k=self.k_dense_layer(y)
        v=self.v_dense_layer(y)

        # 预测模式
        if cache is not None:
            # 合并k和v值
            k=tf.concat([cache['k'],k],axis=1)
            v=tf.concat([cache['v'],v],axis=1)

            cache['k']=k
            cache['v']=v
        # 将q,k,v拆分
        q=self.split_heads(q)
        k=self.split_heads(k)
        v=self.split_heads(v)

        #缩放q以防止q和k之间的点积过大
        depth = (self.hidden_size // self.num_heads)
        q *= depth ** -0.5

        # 计算点积,将k转置
        logits=tf.matmul(q,k,transpose_b=True)
        logits+=bias
        weights=tf.nn.softmax(logits,name='attention_weight')

        # 训练模式使用dropout
        if self.train:
            weights=tf.nn.dropout(weights,1.0-self.attention_dropout)
        attention_outpout=tf.matmul(weights,v)

        # 单头结束，计算多头
        attention_outpout=self.combine_heads(attention_outpout)

        # 使用全连接层输出
        attention_outpout=self.output_dense_layer(attention_outpout)

        return attention_outpout

class SelfAttention(Attention):
  """多头注意力层"""

  def call(self, x, bias, cache=None):
    return super(SelfAttention, self).call(x, x, bias, cache)

3.Embedding

import tensorflow as tf
from official.transformer.model import model_utils
from official.utils.accelerator import tpu as tpu_utils

class EmbeddingSharedWeights(tf.keras.layers.Layer):
    """
    用于encoder，decoder的input embedding，并共享权重
    """
    def __init__(self,vocab_size,hidden_size,method='gater'):
        """

        :param voab_size: 标记字符（tokens）数量，一般小于32000
        :param hidden_size:embedding层神经元数量，一般512或1024
        :param method: gather更适用于CPU,GPU，matmulTPU运算更快
        """
        super(EmbeddingSharedWeights,self).__init__()
        self.vocab_size=vocab_size
        self.hidden_size=hidden_size

        if method not in ('gather','matmul'):
            raise ValueError("method {} must be 'gather' or 'matmul'".format(method))
        self.method=method
    def build(self,_):
        with tf.variable_scope('embedding_and_softmax',reuse=tf.AUTO_REUSE):
            # 创建并初始化权重
            self.shared_weights=tf.get_variable(
                'weights',shape=[self.vocab_size,self.hidden_size],
                initializer=tf.random_normal_initializer(
                    0.,self.hidden_size**-0.5
                )
            )
            self.built=True
    def call(self,x):
        """
        获取embedding后的x
        :param x: int shape[batch_size,length]
        :return: embeddings:shape [batch_size,length,embedding_size]
                 padding: shape[batch_size,length]

                 因为模型默认输入长度必须是固定的，所以需要补长。现在有其它衍变版本transformer-xl可以实现动态更改长度
        """
        with tf.name_scope('embedding'):
            # 创建二进制mask
            mask=tf.to_float(tf.not_equal(x,0))

            if self.method == 'gather':
                embeddings=tf.gather(self.shared_weights)
                embeddings*=tf.expand_dims(mask,-1)
            else:
                embeddings=tpu_utils.embedding_matmul(
                    embedding_table=self.shared_weights,
                    values=tf.cast(x,type=tf.int32),
                    mask=mask
                )
            # 缩放embedding
                embeddings*=self.hidden_size**0.5
        return embeddings

    def linear(self,x):
        """
        输出模型logits
        :param x: shape[batch_size,length,hidden_size]
        :return: shape[batch_size,length,vovab_size]
        """
        with tf.name_scope('presoftmax_linear'):
            batch_size=tf.shape(x)[0]
            length=tf.shape(x)[1]

            x=tf.reshape(x,[-1,self.hidden_size])
            
            # shared_weights 转置
            logits=tf.matmul(x,self.shared_weights,transpose_b=True)

            return tf.reshape(logits, [batch_size, length, self.vocab_size])

4.FFN_layer

import tensorflow as tf

class FeedFowardNetWork(tf.keras.layers.Layer):
    """
    全连接层,共2层
    """
    def __init__(self,hidden_size,filter_size,relu_dropout,train,allow_pad):
        super(FeedFowardNetWork,self).__init__()

        self.hidden_size=hidden_size
        self.filter_size=filter_size
        self.relu_dropout=relu_dropout
        self.train=train

        # 模型默认需要固定长度
        self.all_pad=allow_pad

        self.filter_dense_layer=tf.keras.layers.Dense(
            filter_size,use_bias=True,activation=tf.nn.relu,
            name='filter_layer'
        )
        self.outpout_dense_layer=tf.keras.layers.Dense(
            hidden_size,use_bias=True,name='outpout_layer'
        )
    def call(self,x,padding=None):
        """
        返回全连接层输出
        :param x: shape[batch_size,length,hidden_size]
        :param padding:shape[batch_size,length]
        :return:
        """
        padding=None if not self.all_pad else padding

        # 获取已知shape
        batch_size=tf.shape(x)[0]
        length=tf.shape(x)[1]

        if padding is not None:
            with tf.name_scope('remove_padding'):
                pad_mask=tf.reshape(padding,[-1])
                nopad_ids=tf.to_int32(tf.where(pad_mask<1e-9))

                # 将x维度修改成[batch_size,selt.hidden_size]以移除padding
                x=tf.reshape(x[-1,self.hidden_size])
                x=tf.gather_nd(x,indices=nopad_ids)

                # 扩展一维
                x.set_shape([None, self.hidden_size])
                x = tf.expand_dims(x, axis=0)
        outpout=self.filter_dense_layer(x)

        # 训练模式使用dropout
        if self.train:
            outpout=tf.nn.dropout(outpout,1.0-self.relu_dropout)
        outpout=self.outpout_dense_layer(outpout)

        if padding is not None:
            with tf.name_scope('re_add_padding'):
                # 去除指定维度中，大小为1的
                output=tf.squeeze(outpout,axis=0)
                output = tf.scatter_nd(
                    indices=nopad_ids,
                    updates=output,
                    shape=[batch_size * length, self.hidden_size]
                )
                output = tf.reshape(output, [batch_size, length, self.hidden_size])
                
            return output

5.模型参数

from collections import defaultdict

"""
基本模型参数配置
"""
BASE_PARAMS = defaultdict(
    lambda: None,

    # 输入参数，batch_size的设定要考虑内存情况
    default_batch_size=2048,  #CPU,GPU环境下batch_size大小
    default_batch_size_tpu=32768,
    max_length=256,  # 单个样本最大长度

    # 模型参数
    initializer_gain=1.0,  # 可训练变量初始化
    vocab_size=33708,  # 词表大小
    hidden_size=512,  # 隐藏层神经元数量（全连接层的第二层）
    num_hidden_layers=6,  # encoder，decoder层数
    num_heads=8,  # 多头注意力机制中head数量
    filter_size=2048,  # feedforward连接层中神经元数量

    # dropout参数
    layer_postprocess_dropout=0.1, # 残差连接中dropout参数
    attention_dropout=0.1, # 多头注意力机制中dropout参数
    relu_dropout=0.1, # 全连接层中dropout设置

    # 训练阶段参数
    label_smoothing=0.1, # 平滑参数，用于防止过拟合
    learning_rate=2.0, # 学习率
    learning_rate_decay_rate=1.0, #学习率衰减系数
    learning_rate_warmup_steps=16000,# 模型预热步数

    # adam激活函数参数
    optimizer_adam_beta1=0.9,
    optimizer_adam_beta2=0.997,
    optimizer_adam_epsilon=1e-09,

    # 预测模式参数设置
    extra_decode_length=50,
    beam_size=4,

    # TPU参数设置
    use_tpu=False,
    static_batch=False,
    allow_ffn_pad=True,
)

# 适合TPU环境训练配置
BIG_PARAMS = BASE_PARAMS.copy()
BIG_PARAMS.update(
    
    default_batch_size=4096,
    default_batch_size_tpu=16384,

    hidden_size=1024,
    filter_size=4096,
    num_heads=16,
)

# 多GPU环境训练参数
BASE_MULTI_GPU_PARAMS = BASE_PARAMS.copy()
BASE_MULTI_GPU_PARAMS.update(
    learning_rate_warmup_steps=8000
)

#多TPU环境训练参数
BIG_MULTI_GPU_PARAMS = BIG_PARAMS.copy()
BIG_MULTI_GPU_PARAMS.update(
    layer_postprocess_dropout=0.3,
    learning_rate_warmup_steps=8000
)

# 测试模型参数
TINY_PARAMS = BASE_PARAMS.copy()
TINY_PARAMS.update(
    default_batch_size=1024,
    default_batch_size_tpu=1024,
    hidden_size=32,
    num_heads=4,
    filter_size=256,
)