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conda create -n transform python=3.8 -y
conda activate transform
cd /media/lhy/Transforms/annotatedtransformer
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
# # Uncomment for colab
pip install -q torchdata==0.3.0 torchtext==0.12 spacy==3.2 altair GPUtil -i https://pypi.tuna.tsinghua.edu.cn/simple
python -m spacy download de_core_news_sm
python -m spacy download en_core_web_sm
#或者离线下载
pip install de_core_news_sm-3.2.0-py3-none-any.whl
pip install en_core_web_sm-3.2.0-py3-none-any.whl
class EncoderDecoder(nn.Module): """ A standard Encoder-Decoder architecture. Base for this and many other models. """ def __init__(self, encoder, decoder, src_embed, tgt_embed, generator): super(EncoderDecoder, self).__init__() self.encoder = encoder self.decoder = decoder self.src_embed = src_embed self.tgt_embed = tgt_embed self.generator = generator def forward(self, src, tgt, src_mask, tgt_mask): "Take in and process masked src and target sequences." return self.decode(self.encode(src, src_mask), src_mask, tgt, tgt_mask) def encode(self, src, src_mask): return self.encoder(self.src_embed(src), src_mask) def decode(self, memory, src_mask, tgt, tgt_mask): return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask) class Generator(nn.Module): "Define standard linear + softmax generation step." def __init__(self, d_model, vocab): super(Generator, self).__init__() self.proj = nn.Linear(d_model, vocab) def forward(self, x): return log_softmax(self.proj(x), dim=-1)
Transformer遵循这个整体架构,使用堆叠的自关注层和点方向层,完全连接编码器和解码器层,分别如图1的左半部分和右半部分所示。
def clones(module, N): "Produce N identical layers." return nn.ModuleList([copy.deepcopy(module) for _ in range(N)]) class Encoder(nn.Module): "Core encoder is a stack of N layers" def __init__(self, layer, N): super(Encoder, self).__init__() self.layers = clones(layer, N) self.norm = LayerNorm(layer.size) def forward(self, x, mask): "Pass the input (and mask) through each layer in turn." for layer in self.layers: x = layer(x, mask) return self.norm(x) class LayerNorm(nn.Module): "Construct a layernorm module (See citation for details)." def __init__(self, features, eps=1e-6): super(LayerNorm, self).__init__() self.a_2 = nn.Parameter(torch.ones(features)) self.b_2 = nn.Parameter(torch.zeros(features)) self.eps = eps def forward(self, x): mean = x.mean(-1, keepdim=True) std = x.std(-1, keepdim=True) return self.a_2 * (x - mean) / (std + self.eps) + self.b_2 class SublayerConnection(nn.Module): """ A residual connection followed by a layer norm. Note for code simplicity the norm is first as opposed to last. """ def __init__(self, size, dropout): super(SublayerConnection, self).__init__() self.norm = LayerNorm(size) self.dropout = nn.Dropout(dropout) def forward(self, x, sublayer): "Apply residual connection to any sublayer with the same size." return x + self.dropout(sublayer(self.norm(x))) class EncoderLayer(nn.Module): "Encoder is made up of self-attn and feed forward (defined below)" def __init__(self, size, self_attn, feed_forward, dropout): super(EncoderLayer, self).__init__() self.self_attn = self_attn self.feed_forward = feed_forward self.sublayer = clones(SublayerConnection(size, dropout), 2) self.size = size def forward(self, x, mask): "Follow Figure 1 (left) for connections." x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask)) return self.sublayer[1](x, self.feed_forward) class Decoder(nn.Module): "Generic N layer decoder with masking." def __init__(self, layer, N): super(Decoder, self).__init__() self.layers = clones(layer, N) self.norm = LayerNorm(layer.size) def forward(self, x, memory, src_mask, tgt_mask): for layer in self.layers: x = layer(x, memory, src_mask, tgt_mask) return self.norm(x) class DecoderLayer(nn.Module): "Decoder is made of self-attn, src-attn, and feed forward (defined below)" def __init__(self, size, self_attn, src_attn, feed_forward, dropout): super(DecoderLayer, self).__init__() self.size = size self.self_attn = self_attn self.src_attn = src_attn self.feed_forward = feed_forward self.sublayer = clones(SublayerConnection(size, dropout), 3) def forward(self, x, memory, src_mask, tgt_mask): "Follow Figure 1 (right) for connections." m = memory x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask)) x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask)) return self.sublayer[2](x, self.feed_forward)
def subsequent_mask(size):
"Mask out subsequent positions."
attn_shape = (1, size, size)#batch=1
subsequent_mask = torch.triu(torch.ones(attn_shape), diagonal=1).type(
torch.uint8
)#保留主对角线以上的数据
return subsequent_mask == 0
结果保留主对角线及以下的数据
def attention(query, key, value, mask=None, dropout=None):
"Compute 'Scaled Dot Product Attention'"
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
p_attn = scores.softmax(dim=-1)
if dropout is not None:
p_attn = dropout(p_attn)
return torch.matmul(p_attn, value), p_attn
class MultiHeadedAttention(nn.Module): def __init__(self, h, d_model, dropout=0.1): "Take in model size and number of heads." super(MultiHeadedAttention, self).__init__() assert d_model % h == 0 # We assume d_v always equals d_k self.d_k = d_model // h self.h = h self.linears = clones(nn.Linear(d_model, d_model), 4) self.attn = None self.dropout = nn.Dropout(p=dropout) def forward(self, query, key, value, mask=None): "Implements Figure 2" if mask is not None: # Same mask applied to all h heads. mask = mask.unsqueeze(1) nbatches = query.size(0) # 1) Do all the linear projections in batch from d_model => h x d_k query, key, value = [ lin(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2) for lin, x in zip(self.linears, (query, key, value)) ] # 2) Apply attention on all the projected vectors in batch. x, self.attn = attention( query, key, value, mask=mask, dropout=self.dropout ) # 3) "Concat" using a view and apply a final linear. x = ( x.transpose(1, 2) .contiguous() .view(nbatches, -1, self.h * self.d_k) ) del query del key del value return self.linears[-1](x)
class PositionwiseFeedForward(nn.Module):
"Implements FFN equation."
def __init__(self, d_model, d_ff, dropout=0.1):
super(PositionwiseFeedForward, self).__init__()
self.w_1 = nn.Linear(d_model, d_ff)
self.w_2 = nn.Linear(d_ff, d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
return self.w_2(self.dropout(self.w_1(x).relu()))
class Embeddings(nn.Module):
def __init__(self, d_model, vocab):
super(Embeddings, self).__init__()
self.lut = nn.Embedding(vocab, d_model)
self.d_model = d_model
def forward(self, x):
return self.lut(x) * math.sqrt(self.d_model)
class PositionalEncoding(nn.Module): "Implement the PE function." def __init__(self, d_model, dropout, max_len=5000): super(PositionalEncoding, self).__init__() self.dropout = nn.Dropout(p=dropout) # Compute the positional encodings once in log space. pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len).unsqueeze(1) div_term = torch.exp( torch.arange(0, d_model, 2) * -(math.log(10000.0) / d_model) ) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0) self.register_buffer("pe", pe) def forward(self, x): x = x + self.pe[:, : x.size(1)].requires_grad_(False) return self.dropout(x)
这对应于在第一个warmup_steps训练步骤中线性增加学习率,然后按步数的倒数平方根成比例地降低学习率。我们使用了warmup_steps = 4000。
def rate(step, model_size, factor, warmup):
"""
we have to default the step to 1 for LambdaLR function
to avoid zero raising to negative power.
"""
if step == 0:
step = 1
return factor * (
model_size ** (-0.5) * min(step ** (-0.5), step * warmup ** (-1.5))
)
#---------------------------------4、测试学习率--------------------------------- def example_learning_schedule(): opts = [ [512, 1, 4000], # example 1 [512, 1, 8000], # example 2 [256, 1, 4000], # example 3 ] dummy_model = torch.nn.Linear(1, 1) learning_rates = [] # we have 3 examples in opts list. for idx, example in enumerate(opts): # run 20000 epoch for each example optimizer = torch.optim.Adam( dummy_model.parameters(), lr=1, betas=(0.9, 0.98), eps=1e-9 ) print(optimizer.state_dict()) lr_scheduler = LambdaLR( optimizer=optimizer, lr_lambda=lambda step: rate(step, *example) ) tmp = [] # take 20K dummy training steps, save the learning rate at each step for step in range(20000): tmp.append(optimizer.param_groups[0]["lr"]) optimizer.step() lr_scheduler.step() learning_rates.append(tmp) learning_rates = torch.tensor(learning_rates) # Enable altair to handle more than 5000 rows alt.data_transformers.disable_max_rows() opts_data = pd.concat( [ pd.DataFrame( { "Learning Rate": learning_rates[warmup_idx, :], "model_size:warmup": ["512:4000", "512:8000", "256:4000"][ warmup_idx ], "step": range(20000), } ) for warmup_idx in [0, 1, 2] ] ) chart=(alt.Chart(opts_data) .mark_line() .properties(width=600) .encode(x="step", y="Learning Rate", color="model_size:warmup:N") .interactive()) # 展示数据,调用display()方法 altair_viewer.show(chart)
在训练过程中,我们使用值es =0.1的平滑标签。这损害了困惑,因为模型学的更加不确定,但提高了准确性和BLeU分数。
Kullback-Leibler散度损失。
class LabelSmoothing(nn.Module): "Implement label smoothing." def __init__(self, size, padding_idx, smoothing=0.0): super(LabelSmoothing, self).__init__() self.criterion = nn.KLDivLoss(reduction="sum") self.padding_idx = padding_idx self.confidence = 1.0 - smoothing self.smoothing = smoothing self.size = size self.true_dist = None def forward(self, x, target): assert x.size(1) == self.size true_dist = x.data.clone() true_dist.fill_(self.smoothing / (self.size - 2)) true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence) true_dist[:, self.padding_idx] = 0 mask = torch.nonzero(target.data == self.padding_idx) if mask.dim() > 0: true_dist.index_fill_(0, mask.squeeze(), 0.0) self.true_dist = true_dist return self.criterion(x, true_dist.clone().detach())
#-------------------5、测试 正则化标签平滑------------------------------------- def example_label_smoothing(): crit = LabelSmoothing(5, 0, 0.4) predict = torch.FloatTensor( [ [0, 0.2, 0.7, 0.1, 0], [0, 0.2, 0.7, 0.1, 0], [0, 0.2, 0.7, 0.1, 0], [0, 0.2, 0.7, 0.1, 0], [0, 0.2, 0.7, 0.1, 0], ] ) crit(x=predict.log(), target=torch.LongTensor([2, 1, 0, 3, 3])) LS_data = pd.concat( [ pd.DataFrame( { "target distribution": crit.true_dist[x, y].flatten(), "columns": y, "rows": x, } ) for y in range(5) for x in range(5) ] ) chart= ( alt.Chart(LS_data) .mark_rect(color="Blue", opacity=1) .properties(height=200, width=200) .encode( alt.X("columns:O", title=None), alt.Y("rows:O", title=None), alt.Color( "target distribution:Q", scale=alt.Scale(scheme="viridis") ), ) .interactive() ) # 展示数据,调用display()方法 altair_viewer.show(chart)
离线下载放入缓存
或者修改URL
pip install altair_viewer==0.4.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
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