架构以及架构中的组件

Transform

以下的代码包含：

1. 标准化的示例
2. 残差化的示例

# huggingface
# transformers# https://www.bilibili.com/video/BV1At4y1W75x?spm_id_from=333.999.0.0import copy
import math
from collections import namedtupleimport numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import VariableHypothesis = namedtuple('Hypothesis', ['value', 'score'])def clones(module, n):return nn.ModuleList([copy.deepcopy(module) for _ in range(n)])"""
实现x 的标准化处理（标准化的作用：使x符合正太分布）
"""
class LayerNorm(nn.Module):def __init__(self, feature, eps=1e-6):""":param feature: self-attention 的 x 的大小:param eps:"""super(LayerNorm, self).__init__()self.a_2 = nn.Parameter(torch.ones(feature))self.b_2 = nn.Parameter(torch.zeros(feature))self.eps = epsdef 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"""
残差化的示例
"""
class SublayerConnection(nn.Module):"""这不仅仅做了残差，这是把残差和 layernorm 一起给做了"""def __init__(self, size, dropout=0.1):super(SublayerConnection, self).__init__()# 第一步做 layernorm 这是类的实例化的一种方法self.layer_norm = LayerNorm(size)# 第二步做 dropoutself.dropout = nn.Dropout(p=dropout)def forward(self, x, sublayer):""":param x: 就是self-attention的输入:param sublayer: self-attention层:return:"""return self.dropout(self.layer_norm(x + sublayer(x)))class FeatEmbedding(nn.Module):def __init__(self, d_feat, d_model, dropout):super(FeatEmbedding, self).__init__()self.video_embeddings = nn.Sequential(LayerNorm(d_feat),nn.Dropout(dropout),nn.Linear(d_feat, d_model))def forward(self, x):return self.video_embeddings(x)class TextEmbedding(nn.Module):def __init__(self, vocab_size, d_model):super(TextEmbedding, self).__init__()self.d_model = d_modelself.embed = nn.Embedding(vocab_size, d_model)def forward(self, x):return self.embed(x) * math.sqrt(self.d_model)"""
给一个词向量添加位置编码的示例
"""
class PositionalEncoding(nn.Module):def __init__(self, dim, dropout, max_len=5000):if dim % 2 != 0:raise ValueError("Cannot use sin/cos positional encoding with ""odd dim (got dim={:d})".format(dim))pe = torch.zeros(max_len, dim)position = torch.arange(0, max_len).unsqueeze(1)div_term = torch.exp((torch.arange(0, dim, 2, dtype=torch.float) *-(math.log(10000.0) / dim)))pe[:, 0::2] = torch.sin(position.float() * div_term)pe[:, 1::2] = torch.cos(position.float() * div_term)pe = pe.unsqueeze(1)super(PositionalEncoding, self).__init__()self.register_buffer('pe', pe)self.drop_out = nn.Dropout(p=dropout)self.dim = dimdef forward(self, emb, step=None):emb = emb * math.sqrt(self.dim)if step is None:emb = emb + self.pe[:emb.size(0)]else:emb = emb + self.pe[step]emb = self.drop_out(emb)return emb"""
自注意力机制的实现示例
"""
def self_attention(query, key, value, dropout=None, mask=None):d_k = query.size(-1)scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)# mask的操作在QK之后，softmax之前if mask is not None:mask.cuda()scores = scores.masked_fill(mask == 0, -1e9)self_attn = F.softmax(scores, dim=-1)if dropout is not None:self_attn = dropout(self_attn)return torch.matmul(self_attn, value), self_attn"""
多头--注意力机制的实现示例
"""
class MultiHeadAttention(nn.Module):def __init__(self, head, d_model, dropout=0.1):super(MultiHeadAttention, self).__init__()assert (d_model % head == 0)self.d_k = d_model // headself.head = headself.d_model = d_modelself.linear_query = nn.Linear(d_model, d_model)self.linear_key = nn.Linear(d_model, d_model)self.linear_value = nn.Linear(d_model, d_model)self.linear_out = nn.Linear(d_model, d_model)self.dropout = nn.Dropout(p=dropout)self.attn = Nonedef forward(self, query, key, value, mask=None):if mask is not None:# 多头注意力机制的线性变换层是4维，是把query[batch, frame_num, d_model]变成[batch, -1, head, d_k]# 再1，2维交换变成[batch, head, -1, d_k], 所以mask要在第一维添加一维，与后面的self attention计算维度一样mask = mask.unsqueeze(1)n_batch = query.size(0)# if self.head == 1:#     x, self.attn = self_attention(query, key, value, dropout=self.dropout, mask=mask)# else:#     query = self.linear_query(query).view(n_batch, -1, self.head, self.d_k).transpose(1, 2)  # [b, 8, 32, 64]#     key = self.linear_key(key).view(n_batch, -1, self.head, self.d_k).transpose(1, 2)  # [b, 8, 28, 64]#     value = self.linear_value(value).view(n_batch, -1, self.head, self.d_k).transpose(1, 2)  # [b, 8, 28, 64]##     x, self.attn = self_attention(query, key, value, dropout=self.dropout, mask=mask)#     # 变为三维， 或者说是concat head#     x = x.transpose(1, 2).contiguous().view(n_batch, -1, self.head * self.d_k)query = self.linear_query(query).view(n_batch, -1, self.head, self.d_k).transpose(1, 2)  # [b, 8, 32, 64]key = self.linear_key(key).view(n_batch, -1, self.head, self.d_k).transpose(1, 2)  # [b, 8, 28, 64]value = self.linear_value(value).view(n_batch, -1, self.head, self.d_k).transpose(1, 2)  # [b, 8, 28, 64]x, self.attn = self_attention(query, key, value, dropout=self.dropout, mask=mask)# 变为三维， 或者说是concat headx = x.transpose(1, 2).contiguous().view(n_batch, -1, self.head * self.d_k)return self.linear_out(x)class PositionWiseFeedForward(nn.Module):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.layer_norm = nn.LayerNorm(d_model, eps=1e-6)self.dropout_1 = nn.Dropout(dropout)self.relu = nn.ReLU()self.dropout_2 = nn.Dropout(dropout)def forward(self, x):inter = self.dropout_1(self.relu(self.w_1(self.layer_norm(x))))output = self.dropout_2(self.w_2(inter))return outputclass EncoderLayer(nn.Module):def __init__(self, size, attn, feed_forward, dropout=0.1):super(EncoderLayer, self).__init__()self.attn = attnself.feed_forward = feed_forwardself.sublayer_connection = clones(SublayerConnection(size, dropout), 2)def forward(self, x, mask):x = self.sublayer_connection[0](x, lambda x: self.attn(x, x, x, mask))return self.sublayer_connection[1](x, self.feed_forward)class EncoderLayerNoAttention(nn.Module):def __init__(self, size, attn, feed_forward, dropout=0.1):super(EncoderLayerNoAttention, self).__init__()self.attn = attnself.feed_forward = feed_forwardself.sublayer_connection = clones(SublayerConnection(size, dropout), 2)def forward(self, x, mask):return self.sublayer_connection[1](x, self.feed_forward)class DecoderLayer(nn.Module):def __init__(self, size, attn, feed_forward, sublayer_num, dropout=0.1):super(DecoderLayer, self).__init__()self.attn = attnself.feed_forward = feed_forwardself.sublayer_connection = clones(SublayerConnection(size, dropout), sublayer_num)def forward(self, x, memory, src_mask, trg_mask, r2l_memory=None, r2l_trg_mask=None):x = self.sublayer_connection[0](x, lambda x: self.attn(x, x, x, trg_mask))x = self.sublayer_connection[1](x, lambda x: self.attn(x, memory, memory, src_mask))if r2l_memory is not None:x = self.sublayer_connection[-2](x, lambda x: self.attn(x, r2l_memory, r2l_memory, r2l_trg_mask))return self.sublayer_connection[-1](x, self.feed_forward)class Encoder(nn.Module):def __init__(self, n, encoder_layer):super(Encoder, self).__init__()self.encoder_layer = clones(encoder_layer, n)def forward(self, x, src_mask):for layer in self.encoder_layer:x = layer(x, src_mask)return xclass R2L_Decoder(nn.Module):def __init__(self, n, decoder_layer):super(R2L_Decoder, self).__init__()self.decoder_layer = clones(decoder_layer, n)def forward(self, x, memory, src_mask, r2l_trg_mask):for layer in self.decoder_layer:x = layer(x, memory, src_mask, r2l_trg_mask)return xclass L2R_Decoder(nn.Module):def __init__(self, n, decoder_layer):super(L2R_Decoder, self).__init__()self.decoder_layer = clones(decoder_layer, n)def forward(self, x, memory, src_mask, trg_mask, r2l_memory, r2l_trg_mask):for layer in self.decoder_layer:x = layer(x, memory, src_mask, trg_mask, r2l_memory, r2l_trg_mask)return xdef pad_mask(src, r2l_trg, trg, pad_idx):if isinstance(src, tuple):if len(src) == 4:src_image_mask = (src[0][:, :, 0] != pad_idx).unsqueeze(1)src_motion_mask = (src[1][:, :, 0] != pad_idx).unsqueeze(1)src_object_mask = (src[2][:, :, 0] != pad_idx).unsqueeze(1)src_rel_mask = (src[3][:, :, 0] != pad_idx).unsqueeze(1)enc_src_mask = (src_image_mask, src_motion_mask, src_object_mask, src_rel_mask)dec_src_mask_1 = src_image_mask & src_motion_maskdec_src_mask_2 = src_image_mask & src_motion_mask & src_object_mask & src_rel_maskdec_src_mask = (dec_src_mask_1, dec_src_mask_2)src_mask = (enc_src_mask, dec_src_mask)if len(src) == 3:src_image_mask = (src[0][:, :, 0] != pad_idx).unsqueeze(1)src_motion_mask = (src[1][:, :, 0] != pad_idx).unsqueeze(1)src_object_mask = (src[2][:, :, 0] != pad_idx).unsqueeze(1)enc_src_mask = (src_image_mask, src_motion_mask, src_object_mask)dec_src_mask = src_image_mask & src_motion_masksrc_mask = (enc_src_mask, dec_src_mask)if len(src) == 2:src_image_mask = (src[0][:, :, 0] != pad_idx).unsqueeze(1)src_motion_mask = (src[1][:, :, 0] != pad_idx).unsqueeze(1)enc_src_mask = (src_image_mask, src_motion_mask)dec_src_mask = src_image_mask & src_motion_masksrc_mask = (enc_src_mask, dec_src_mask)else:src_mask = (src[:, :, 0] != pad_idx).unsqueeze(1)if trg is not None:if isinstance(src_mask, tuple):trg_mask = (trg != pad_idx).unsqueeze(1) & subsequent_mask(trg.size(1)).type_as(src_image_mask.data)r2l_pad_mask = (r2l_trg != pad_idx).unsqueeze(1).type_as(src_image_mask.data)r2l_trg_mask = r2l_pad_mask & subsequent_mask(r2l_trg.size(1)).type_as(src_image_mask.data)return src_mask, r2l_pad_mask, r2l_trg_mask, trg_maskelse:trg_mask = (trg != pad_idx).unsqueeze(1) & subsequent_mask(trg.size(1)).type_as(src_mask.data)r2l_pad_mask = (r2l_trg != pad_idx).unsqueeze(1).type_as(src_mask.data)r2l_trg_mask = r2l_pad_mask & subsequent_mask(r2l_trg.size(1)).type_as(src_mask.data)return src_mask, r2l_pad_mask, r2l_trg_mask, trg_mask  # src_mask[batch, 1, lens]  trg_mask[batch, 1, lens]else:return src_maskdef subsequent_mask(size):"""Mask out subsequent positions."""attn_shape = (1, size, size)mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')return (torch.from_numpy(mask) == 0).cuda()class Generator(nn.Module):def __init__(self, d_model, vocab_size):super(Generator, self).__init__()self.linear = nn.Linear(d_model, vocab_size)def forward(self, x):return F.log_softmax(self.linear(x), dim=-1)class ABDTransformer(nn.Module):def __init__(self, vocab, d_feat, d_model, d_ff, n_heads, n_layers, dropout, feature_mode,device='cuda', n_heads_big=128):super(ABDTransformer, self).__init__()self.vocab = vocabself.device = deviceself.feature_mode = feature_modec = copy.deepcopy# attn_no_heads = MultiHeadAttention(1, d_model, dropout)attn = MultiHeadAttention(n_heads, d_model, dropout)attn_big = MultiHeadAttention(n_heads_big, d_model, dropout)# attn_big2 = MultiHeadAttention(10, d_model, dropout)feed_forward = PositionWiseFeedForward(d_model, d_ff)if feature_mode == 'one':self.src_embed = FeatEmbedding(d_feat, d_model, dropout)elif feature_mode == 'two':self.image_src_embed = FeatEmbedding(d_feat[0], d_model, dropout)self.motion_src_embed = FeatEmbedding(d_feat[1], d_model, dropout)elif feature_mode == 'three':self.image_src_embed = FeatEmbedding(d_feat[0], d_model, dropout)self.motion_src_embed = FeatEmbedding(d_feat[1], d_model, dropout)self.object_src_embed = FeatEmbedding(d_feat[2], d_model, dropout)elif feature_mode == 'four':self.image_src_embed = FeatEmbedding(d_feat[0], d_model, dropout)self.motion_src_embed = FeatEmbedding(d_feat[1], d_model, dropout)self.object_src_embed = FeatEmbedding(d_feat[2], d_model, dropout)self.rel_src_embed = FeatEmbedding(d_feat[3], d_model, dropout)self.trg_embed = TextEmbedding(vocab.n_vocabs, d_model)self.pos_embed = PositionalEncoding(d_model, dropout)# self.encoder_no_heads = Encoder(n_layers, EncoderLayer(d_model, c(attn_no_heads), c(feed_forward), dropout))self.encoder = Encoder(n_layers, EncoderLayer(d_model, c(attn), c(feed_forward), dropout))self.encoder_big = Encoder(n_layers, EncoderLayer(d_model, c(attn_big), c(feed_forward), dropout))# self.encoder_big2 = Encoder(n_layers, EncoderLayer(d_model, c(attn_big2), c(feed_forward), dropout))self.encoder_no_attention = Encoder(n_layers,EncoderLayerNoAttention(d_model, c(attn), c(feed_forward), dropout))self.r2l_decoder = R2L_Decoder(n_layers, DecoderLayer(d_model, c(attn), c(feed_forward),sublayer_num=3, dropout=dropout))self.l2r_decoder = L2R_Decoder(n_layers, DecoderLayer(d_model, c(attn), c(feed_forward),sublayer_num=4, dropout=dropout))self.generator = Generator(d_model, vocab.n_vocabs)def encode(self, src, src_mask, feature_mode_two=False):if self.feature_mode == 'two':x1 = self.image_src_embed(src[0])x1 = self.pos_embed(x1)x1 = self.encoder_big(x1, src_mask[0])x2 = self.motion_src_embed(src[1])x2 = self.pos_embed(x2)x2 = self.encoder_big(x2, src_mask[1])return x1 + x2if feature_mode_two:x1 = self.image_src_embed(src[0])x1 = self.pos_embed(x1)x1 = self.encoder_big(x1, src_mask[0])x2 = self.motion_src_embed(src[1])x2 = self.pos_embed(x2)x2 = self.encoder_big(x2, src_mask[1])return x1 + x2if self.feature_mode == 'one':x = self.src_embed(src)x = self.pos_embed(x)return self.encoder(x, src_mask)elif self.feature_mode == 'two':x1 = self.image_src_embed(src[0])x1 = self.pos_embed(x1)x1 = self.encoder_big(x1, src_mask[0])x2 = self.motion_src_embed(src[1])x2 = self.pos_embed(x2)x2 = self.encoder_big(x2, src_mask[1])return x1 + x2elif self.feature_mode == 'three':x1 = self.image_src_embed(src[0])x1 = self.pos_embed(x1)x1 = self.encoder(x1, src_mask[0])x2 = self.motion_src_embed(src[1])x2 = self.pos_embed(x2)x2 = self.encoder(x2, src_mask[1])x3 = self.object_src_embed(src[2])x3 = self.pos_embed(x3)x3 = self.encoder(x3, src_mask[2])return x1 + x2 + x3elif self.feature_mode == 'four':x1 = self.image_src_embed(src[0])x1 = self.pos_embed(x1)x1 = self.encoder(x1, src_mask[0])x2 = self.motion_src_embed(src[1])x2 = self.pos_embed(x2)x2 = self.encoder(x2, src_mask[1])x3 = self.object_src_embed(src[2])# x3 = self.pos_embed(x3)x3 = self.encoder(x3, src_mask[2])# x3 = self.encoder_no_attention(x3, src_mask[2])x4 = self.rel_src_embed(src[3])# x4 = self.pos_embed(x4)# x4 = self.encoder_no_# heads(x4, src_mask[3])x4 = self.encoder_no_attention(x4, src_mask[3])# x4 = self.encoder(x4, src_mask[3])return x1 + x2 + x3 + x4def r2l_decode(self, r2l_trg, memory, src_mask, r2l_trg_mask):x = self.trg_embed(r2l_trg)x = self.pos_embed(x)return self.r2l_decoder(x, memory, src_mask, r2l_trg_mask)def l2r_decode(self, trg, memory, src_mask, trg_mask, r2l_memory, r2l_trg_mask):x = self.trg_embed(trg)x = self.pos_embed(x)return self.l2r_decoder(x, memory, src_mask, trg_mask, r2l_memory, r2l_trg_mask)def forward(self, src, r2l_trg, trg, mask):src_mask, r2l_pad_mask, r2l_trg_mask, trg_mask = maskif self.feature_mode == 'one':encoding_outputs = self.encode(src, src_mask)r2l_outputs = self.r2l_decode(r2l_trg, encoding_outputs, src_mask, r2l_trg_mask)l2r_outputs = self.l2r_decode(trg, encoding_outputs, src_mask, trg_mask, r2l_outputs, r2l_pad_mask)elif self.feature_mode == 'two' or 'three' or 'four':enc_src_mask, dec_src_mask = src_maskr2l_encoding_outputs = self.encode(src, enc_src_mask, feature_mode_two=True)encoding_outputs = self.encode(src, enc_src_mask)r2l_outputs = self.r2l_decode(r2l_trg, r2l_encoding_outputs, dec_src_mask[0], r2l_trg_mask)l2r_outputs = self.l2r_decode(trg, encoding_outputs, dec_src_mask[1], trg_mask, r2l_outputs, r2l_pad_mask)# r2l_outputs = self.r2l_decode(r2l_trg, encoding_outputs, dec_src_mask, r2l_trg_mask)# l2r_outputs = self.l2r_decode(trg, encoding_outputs, dec_src_mask, trg_mask, None, None)else:raise "没有输出"r2l_pred = self.generator(r2l_outputs)l2r_pred = self.generator(l2r_outputs)return r2l_pred, l2r_preddef greedy_decode(self, batch_size, src_mask, memory, max_len):eos_idx = self.vocab.word2idx['<S>']r2l_hidden = Nonewith torch.no_grad():output = torch.ones(batch_size, 1).fill_(eos_idx).long().cuda()for i in range(max_len + 2 - 1):trg_mask = subsequent_mask(output.size(1))dec_out = self.r2l_decode(output, memory, src_mask, trg_mask)  # batch, len, d_modelr2l_hidden = dec_outpred = self.generator(dec_out)  # batch, len, n_vocabsnext_word = pred[:, -1].max(dim=-1)[1].unsqueeze(1)  # pred[:, -1]([batch, n_vocabs])output = torch.cat([output, next_word], dim=-1)return r2l_hidden, output# beam search 必用的def r2l_beam_search_decode(self, batch_size, src, src_mask, model_encodings, beam_size, max_len):end_symbol = self.vocab.word2idx['<S>']start_symbol = self.vocab.word2idx['<S>']r2l_outputs = None# 1.1 Setup Src"src has shape (batch_size, sent_len)""src_mask has shape (batch_size, 1, sent_len)"# src_mask = (src[:, :, 0] != self.vocab.word2idx['<PAD>']).unsqueeze(-2)  # TODO Untested"model_encodings has shape (batch_size, sentence_len, d_model)"# model_encodings = self.encode(src, src_mask)# 1.2 Setup Tgt Hypothesis Tracking"hypothesis is List(4 bt)[(cur beam_sz, dec_sent_len)], init: List(4 bt)[(1 init_beam_sz, dec_sent_len)]""hypotheses[i] is shape (cur beam_sz, dec_sent_len)"hypotheses = [copy.deepcopy(torch.full((1, 1), start_symbol, dtype=torch.long,device=self.device)) for _ in range(batch_size)]"List after init: List 4 bt of List of len max_len_completed, init: List of len 4 bt of []"completed_hypotheses = [copy.deepcopy([]) for _ in range(batch_size)]"List len batch_sz of shape (cur beam_sz), init: List(4 bt)[(1 init_beam_sz)]""hyp_scores[i] is shape (cur beam_sz)"hyp_scores = [copy.deepcopy(torch.full((1,), 0, dtype=torch.float, device=self.device))for _ in range(batch_size)]  # probs are log_probs must be init at 0.# 2. Iterate: Generate one char at a time until maxlenfor iter in range(max_len + 1):if all([len(completed_hypotheses[i]) == beam_size for i in range(batch_size)]):break# 2.1 Setup the batch. Since we use beam search, each batch has a variable number (called cur_beam_size)# between 0 and beam_size of hypotheses live at any moment. We decode all hypotheses for all batches at# the same time, so we must copy the src_encodings, src_mask, etc the appropriate number fo times for# the number of hypotheses for each example. We keep track of the number of live hypotheses for each example.# We run all hypotheses for all examples together through the decoder and log-softmax,# and then use `torch.split` to get the appropriate number of hypotheses for each example in the end.cur_beam_sizes, last_tokens, model_encodings_l, src_mask_l = [], [], [], []for i in range(batch_size):if hypotheses[i] is None:cur_beam_sizes += [0]continuecur_beam_size, decoded_len = hypotheses[i].shapecur_beam_sizes += [cur_beam_size]last_tokens += [hypotheses[i]]model_encodings_l += [model_encodings[i:i + 1]] * cur_beam_sizesrc_mask_l += [src_mask[i:i + 1]] * cur_beam_size"shape (sum(4 bt * cur_beam_sz_i), 1 dec_sent_len, 128 d_model)"model_encodings_cur = torch.cat(model_encodings_l, dim=0)src_mask_cur = torch.cat(src_mask_l, dim=0)y_tm1 = torch.cat(last_tokens, dim=0)"shape (sum(4 bt * cur_beam_sz_i), 1 dec_sent_len, 128 d_model)"if self.feature_mode == 'one':out = self.r2l_decode(Variable(y_tm1).to(self.device), model_encodings_cur, src_mask_cur,Variable(subsequent_mask(y_tm1.size(-1)).type_as(src.data)).to(self.device))elif self.feature_mode == 'two' or 'three' or 'four':out = self.r2l_decode(Variable(y_tm1).to(self.device), model_encodings_cur, src_mask_cur,Variable(subsequent_mask(y_tm1.size(-1)).type_as(src[0].data)).to(self.device))r2l_outputs = out"shape (sum(4 bt * cur_beam_sz_i), 1 dec_sent_len, 50002 vocab_sz)"log_prob = self.generator(out[:, -1, :]).unsqueeze(1)"shape (sum(4 bt * cur_beam_sz_i), 1 dec_sent_len, 50002 vocab_sz)"_, decoded_len, vocab_sz = log_prob.shape# log_prob = log_prob.reshape(batch_size, cur_beam_size, decoded_len, vocab_sz)"shape List(4 bt)[(cur_beam_sz_i, dec_sent_len, 50002 vocab_sz)]""log_prob[i] is (cur_beam_sz_i, dec_sent_len, 50002 vocab_sz)"log_prob = torch.split(log_prob, cur_beam_sizes, dim=0)# 2.2 Now we process each example in the batch. Note that the example may have already finished processing before# other examples (no more hypotheses to try), in which case we continuenew_hypotheses, new_hyp_scores = [], []for i in range(batch_size):if hypotheses[i] is None or len(completed_hypotheses[i]) >= beam_size:new_hypotheses += [None]new_hyp_scores += [None]continue# 2.2.1 We compute the cumulative scores for each live hypotheses for the example# hyp_scores is the old scores for the previous stage, and `log_prob` are the new probs for# this stage. Since they are log probs, we sum them instaed of multiplying them.# The .view(-1) forces all the hypotheses into one dimension. The shape of this dimension is# cur_beam_sz * vocab_sz (ex: 5 * 50002). So after getting the topk from it, we can recover the# generating sentence and the next word using: ix // vocab_sz, ix % vocab_sz.cur_beam_sz_i, dec_sent_len, vocab_sz = log_prob[i].shape"shape (vocab_sz,)"cumulative_hyp_scores_i = (hyp_scores[i].unsqueeze(-1).unsqueeze(-1).expand((cur_beam_sz_i, 1, vocab_sz)) + log_prob[i]).view(-1)# 2.2.2 We get the topk values in cumulative_hyp_scores_i and compute the current (generating) sentence# and the next word using: ix // vocab_sz, ix % vocab_sz."shape (cur_beam_sz,)"live_hyp_num_i = beam_size - len(completed_hypotheses[i])"shape (cur_beam_sz,). Vals are between 0 and 50002 vocab_sz"top_cand_hyp_scores, top_cand_hyp_pos = torch.topk(cumulative_hyp_scores_i, k=live_hyp_num_i)"shape (cur_beam_sz,). prev_hyp_ids vals are 0 <= val < cur_beam_sz. hyp_word_ids vals are 0 <= val < vocab_len"prev_hyp_ids, hyp_word_ids = top_cand_hyp_pos // self.vocab.n_vocabs, \top_cand_hyp_pos % self.vocab.n_vocabs# 2.2.3 For each of the topk words, we append the new word to the current (generating) sentence# We add this to new_hypotheses_i and add its corresponding total score to new_hyp_scores_inew_hypotheses_i, new_hyp_scores_i = [], []  # Removed live_hyp_ids_i, which is used in the LSTM decoder to track live hypothesis idsfor prev_hyp_id, hyp_word_id, cand_new_hyp_score in zip(prev_hyp_ids, hyp_word_ids,top_cand_hyp_scores):prev_hyp_id, hyp_word_id, cand_new_hyp_score = \prev_hyp_id.item(), hyp_word_id.item(), cand_new_hyp_score.item()new_hyp_sent = torch.cat((hypotheses[i][prev_hyp_id], torch.tensor([hyp_word_id], device=self.device)))if hyp_word_id == end_symbol:completed_hypotheses[i].append(Hypothesis(value=[self.vocab.idx2word[a.item()] for a in new_hyp_sent[1:-1]],score=cand_new_hyp_score))else:new_hypotheses_i.append(new_hyp_sent.unsqueeze(-1))new_hyp_scores_i.append(cand_new_hyp_score)# 2.2.4 We may find that the hypotheses_i for some example in the batch# is empty - we have fully processed that example. We use None as a sentinel in this case.# Above, the loops gracefully handle None examples.if len(new_hypotheses_i) > 0:hypotheses_i = torch.cat(new_hypotheses_i, dim=-1).transpose(0, -1).to(self.device)hyp_scores_i = torch.tensor(new_hyp_scores_i, dtype=torch.float, device=self.device)else:hypotheses_i, hyp_scores_i = None, Nonenew_hypotheses += [hypotheses_i]new_hyp_scores += [hyp_scores_i]# print(new_hypotheses, new_hyp_scores)hypotheses, hyp_scores = new_hypotheses, new_hyp_scores# 2.3 Finally, we do some postprocessing to get our final generated candidate sentences.# Sometimes, we may get to max_len of a sentence and still not generate the </s> end token.# In this case, the partial sentence we have generated will not be added to the completed_hypotheses# automatically, and we have to manually add it in. We add in as many as necessary so that there are# `beam_size` completed hypotheses for each example.# Finally, we sort each completed hypothesis by score.for i in range(batch_size):hyps_to_add = beam_size - len(completed_hypotheses[i])if hyps_to_add > 0:scores, ix = torch.topk(hyp_scores[i], k=hyps_to_add)for score, id in zip(scores, ix):completed_hypotheses[i].append(Hypothesis(value=[self.vocab.idx2word[a.item()] for a in hypotheses[i][id][1:]],score=score))completed_hypotheses[i].sort(key=lambda hyp: hyp.score, reverse=True)return r2l_outputs, completed_hypothesesdef beam_search_decode(self, src, beam_size, max_len):"""An Implementation of Beam Search for the Transformer Model.Beam search is performed in a batched manner. Each example in a batch generates `beam_size` hypotheses.We return a list (len: batch_size) of list (len: beam_size) of Hypothesis, which contain our output decoded sentencesand their scores.:param src: shape (sent_len, batch_size). Each val is 0 < val < len(vocab_dec). The input tokens to the decoder.:param max_len: the maximum length to decode:param beam_size: the beam size to use:return completed_hypotheses: A List of length batch_size, each containing a List of beam_size Hypothesis objects.Hypothesis is a named Tuple, its first entry is "value" and is a List of strings which contains the translated word(one string is one word token). The second entry is "score" and it is the log-prob score for this translated sentence.Note: Below I note "4 bt", "5 beam_size" as the shapes of objects. 4, 5 are default values. Actual values may differ."""# 1. Setupstart_symbol = self.vocab.word2idx['<S>']end_symbol = self.vocab.word2idx['<S>']# 1.1 Setup Src"src has shape (batch_size, sent_len)""src_mask has shape (batch_size, 1, sent_len)"# src_mask = (src[:, :, 0] != self.vocab.word2idx['<PAD>']).unsqueeze(-2)  # TODO Untestedsrc_mask = pad_mask(src, r2l_trg=None, trg=None, pad_idx=self.vocab.word2idx['<PAD>'])"model_encodings has shape (batch_size, sentence_len, d_model)"if self.feature_mode == 'one':batch_size = src.shape[0]model_encodings = self.encode(src, src_mask)r2l_memory, r2l_completed_hypotheses = self.r2l_beam_search_decode(batch_size, src, src_mask,model_encodings=model_encodings,beam_size=beam_size, max_len=max_len)elif self.feature_mode == 'two' or 'three' or 'four':batch_size = src[0].shape[0]enc_src_mask = src_mask[0]dec_src_mask = src_mask[1]r2l_model_encodings = self.encode(src, enc_src_mask, feature_mode_two=True)# model_encodings = r2l_model_encodingsmodel_encodings = self.encode(src, enc_src_mask)r2l_memory, r2l_completed_hypotheses = self.r2l_beam_search_decode(batch_size, src, dec_src_mask[0],model_encodings=r2l_model_encodings,beam_size=beam_size, max_len=max_len)# 1.2 Setup r2l target output# r2l_memory, r2l_completed_hypotheses = self.r2l_beam_search_decode(batch_size, src, src_mask,#                                                                    model_encodings=model_encodings,#                                                                    beam_size=1, max_len=max_len)# r2l_memory, r2l_completed_hypotheses = self.greedy_decode(batch_size, src_mask, model_encodings, max_len)# beam_r2l_memory = [copy.deepcopy(r2l_memory) for _ in range(beam_size)]# 1.3 Setup Tgt Hypothesis Tracking"hypothesis is List(4 bt)[(cur beam_sz, dec_sent_len)], init: List(4 bt)[(1 init_beam_sz, dec_sent_len)]""hypotheses[i] is shape (cur beam_sz, dec_sent_len)"hypotheses = [copy.deepcopy(torch.full((1, 1), start_symbol, dtype=torch.long,device=self.device)) for _ in range(batch_size)]"List after init: List 4 bt of List of len max_len_completed, init: List of len 4 bt of []"completed_hypotheses = [copy.deepcopy([]) for _ in range(batch_size)]"List len batch_sz of shape (cur beam_sz), init: List(4 bt)[(1 init_beam_sz)]""hyp_scores[i] is shape (cur beam_sz)"hyp_scores = [copy.deepcopy(torch.full((1,), 0, dtype=torch.float, device=self.device))for _ in range(batch_size)]  # probs are log_probs must be init at 0.# 2. Iterate: Generate one char at a time until maxlenfor iter in range(max_len + 1):if all([len(completed_hypotheses[i]) == beam_size for i in range(batch_size)]):break# 2.1 Setup the batch. Since we use beam search, each batch has a variable number (called cur_beam_size)# between 0 and beam_size of hypotheses live at any moment. We decode all hypotheses for all batches at# the same time, so we must copy the src_encodings, src_mask, etc the appropriate number fo times for# the number of hypotheses for each example. We keep track of the number of live hypotheses for each example.# We run all hypotheses for all examples together through the decoder and log-softmax,# and then use `torch.split` to get the appropriate number of hypotheses for each example in the end.cur_beam_sizes, last_tokens, model_encodings_l, src_mask_l, r2l_memory_l = [], [], [], [], []for i in range(batch_size):if hypotheses[i] is None:cur_beam_sizes += [0]continuecur_beam_size, decoded_len = hypotheses[i].shapecur_beam_sizes += [cur_beam_size]last_tokens += [hypotheses[i]]model_encodings_l += [model_encodings[i:i + 1]] * cur_beam_sizeif self.feature_mode == 'one':src_mask_l += [src_mask[i:i + 1]] * cur_beam_sizeelif self.feature_mode == 'two' or 'three' or 'four':src_mask_l += [dec_src_mask[1][i:i + 1]] * cur_beam_sizer2l_memory_l += [r2l_memory[i: i + 1]] * cur_beam_size"shape (sum(4 bt * cur_beam_sz_i), 1 dec_sent_len, 128 d_model)"model_encodings_cur = torch.cat(model_encodings_l, dim=0)src_mask_cur = torch.cat(src_mask_l, dim=0)y_tm1 = torch.cat(last_tokens, dim=0)r2l_memory_cur = torch.cat(r2l_memory_l, dim=0)"shape (sum(4 bt * cur_beam_sz_i), 1 dec_sent_len, 128 d_model)"if self.feature_mode == 'one':out = self.l2r_decode(Variable(y_tm1).to(self.device), model_encodings_cur, src_mask_cur,Variable(subsequent_mask(y_tm1.size(-1)).type_as(src.data)).to(self.device),r2l_memory_cur, r2l_trg_mask=None)elif self.feature_mode == 'two' or 'three' or 'four':out = self.l2r_decode(Variable(y_tm1).to(self.device), model_encodings_cur, src_mask_cur,Variable(subsequent_mask(y_tm1.size(-1)).type_as(src[0].data)).to(self.device),r2l_memory_cur, r2l_trg_mask=None)"shape (sum(4 bt * cur_beam_sz_i), 1 dec_sent_len, 50002 vocab_sz)"log_prob = self.generator(out[:, -1, :]).unsqueeze(1)"shape (sum(4 bt * cur_beam_sz_i), 1 dec_sent_len, 50002 vocab_sz)"_, decoded_len, vocab_sz = log_prob.shape# log_prob = log_prob.reshape(batch_size, cur_beam_size, decoded_len, vocab_sz)"shape List(4 bt)[(cur_beam_sz_i, dec_sent_len, 50002 vocab_sz)]""log_prob[i] is (cur_beam_sz_i, dec_sent_len, 50002 vocab_sz)"log_prob = torch.split(log_prob, cur_beam_sizes, dim=0)# 2.2 Now we process each example in the batch. Note that the example may have already finished processing before# other examples (no more hypotheses to try), in which case we continuenew_hypotheses, new_hyp_scores = [], []for i in range(batch_size):if hypotheses[i] is None or len(completed_hypotheses[i]) >= beam_size:new_hypotheses += [None]new_hyp_scores += [None]continue# 2.2.1 We compute the cumulative scores for each live hypotheses for the example# hyp_scores is the old scores for the previous stage, and `log_prob` are the new probs for# this stage. Since they are log probs, we sum them instaed of multiplying them.# The .view(-1) forces all the hypotheses into one dimension. The shape of this dimension is# cur_beam_sz * vocab_sz (ex: 5 * 50002). So after getting the topk from it, we can recover the# generating sentence and the next word using: ix // vocab_sz, ix % vocab_sz.cur_beam_sz_i, dec_sent_len, vocab_sz = log_prob[i].shape"shape (vocab_sz,)"cumulative_hyp_scores_i = (hyp_scores[i].unsqueeze(-1).unsqueeze(-1).expand((cur_beam_sz_i, 1, vocab_sz)) + log_prob[i]).view(-1)# 2.2.2 We get the topk values in cumulative_hyp_scores_i and compute the current (generating) sentence# and the next word using: ix // vocab_sz, ix % vocab_sz."shape (cur_beam_sz,)"live_hyp_num_i = beam_size - len(completed_hypotheses[i])"shape (cur_beam_sz,). Vals are between 0 and 50002 vocab_sz"top_cand_hyp_scores, top_cand_hyp_pos = torch.topk(cumulative_hyp_scores_i, k=live_hyp_num_i)"shape (cur_beam_sz,). prev_hyp_ids vals are 0 <= val < cur_beam_sz. hyp_word_ids vals are 0 <= val < vocab_len"prev_hyp_ids, hyp_word_ids = top_cand_hyp_pos // self.vocab.n_vocabs, \top_cand_hyp_pos % self.vocab.n_vocabs# 2.2.3 For each of the topk words, we append the new word to the current (generating) sentence# We add this to new_hypotheses_i and add its corresponding total score to new_hyp_scores_inew_hypotheses_i, new_hyp_scores_i = [], []  # Removed live_hyp_ids_i, which is used in the LSTM decoder to track live hypothesis idsfor prev_hyp_id, hyp_word_id, cand_new_hyp_score in zip(prev_hyp_ids, hyp_word_ids,top_cand_hyp_scores):prev_hyp_id, hyp_word_id, cand_new_hyp_score = \prev_hyp_id.item(), hyp_word_id.item(), cand_new_hyp_score.item()new_hyp_sent = torch.cat((hypotheses[i][prev_hyp_id], torch.tensor([hyp_word_id], device=self.device)))if hyp_word_id == end_symbol:completed_hypotheses[i].append(Hypothesis(value=[self.vocab.idx2word[a.item()] for a in new_hyp_sent[1:-1]],score=cand_new_hyp_score))else:new_hypotheses_i.append(new_hyp_sent.unsqueeze(-1))new_hyp_scores_i.append(cand_new_hyp_score)# 2.2.4 We may find that the hypotheses_i for some example in the batch# is empty - we have fully processed that example. We use None as a sentinel in this case.# Above, the loops gracefully handle None examples.if len(new_hypotheses_i) > 0:hypotheses_i = torch.cat(new_hypotheses_i, dim=-1).transpose(0, -1).to(self.device)hyp_scores_i = torch.tensor(new_hyp_scores_i, dtype=torch.float, device=self.device)else:hypotheses_i, hyp_scores_i = None, Nonenew_hypotheses += [hypotheses_i]new_hyp_scores += [hyp_scores_i]# print(new_hypotheses, new_hyp_scores)hypotheses, hyp_scores = new_hypotheses, new_hyp_scores# 2.3 Finally, we do some postprocessing to get our final generated candidate sentences.# Sometimes, we may get to max_len of a sentence and still not generate the </s> end token.# In this case, the partial sentence we have generated will not be added to the completed_hypotheses# automatically, and we have to manually add it in. We add in as many as necessary so that there are# `beam_size` completed hypotheses for each example.# Finally, we sort each completed hypothesis by score.for i in range(batch_size):hyps_to_add = beam_size - len(completed_hypotheses[i])if hyps_to_add > 0:scores, ix = torch.topk(hyp_scores[i], k=hyps_to_add)for score, id in zip(scores, ix):completed_hypotheses[i].append(Hypothesis(value=[self.vocab.idx2word[a.item()] for a in hypotheses[i][id][1:]],score=score))completed_hypotheses[i].sort(key=lambda hyp: hyp.score, reverse=True)# print('completed_hypotheses', completed_hypotheses)return r2l_completed_hypotheses, completed_hypotheses