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from __future__ import absolute_import |
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from __future__ import division |
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from __future__ import print_function |
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import copy |
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import math |
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import numpy as np |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from .Generator import pack_wrapper, GenModel |
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def clones(module, N): |
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return nn.ModuleList([copy.deepcopy(module) for _ in range(N)]) |
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def attention(query, key, value, mask=None, dropout=None): |
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d_k = query.size(-1) |
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scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k) |
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if mask is not None: |
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scores = scores.masked_fill(mask == 0, -1e9) |
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p_attn = F.softmax(scores, dim=-1) |
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if dropout is not None: |
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p_attn = dropout(p_attn) |
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return torch.matmul(p_attn, value), p_attn |
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def subsequent_mask(size): |
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attn_shape = (1, size, size) |
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subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8') |
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return torch.from_numpy(subsequent_mask) == 0 |
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class Transformer(nn.Module): |
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def __init__(self, encoder, decoder, src_embed, tgt_embed): |
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super(Transformer, self).__init__() |
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self.encoder = encoder |
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self.decoder = decoder |
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self.src_embed = src_embed |
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self.tgt_embed = tgt_embed |
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def forward(self, src, tgt, src_mask, tgt_mask): |
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return self.decode(self.encode(src, src_mask), src_mask, tgt, tgt_mask) |
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def encode(self, src, src_mask): |
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return self.encoder(self.src_embed(src), src_mask) |
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def decode(self, hidden_states, src_mask, tgt, tgt_mask): |
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memory = None |
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return self.decoder(self.tgt_embed(tgt), hidden_states, src_mask, tgt_mask, memory) |
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class Encoder(nn.Module): |
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def __init__(self, layer, N): |
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super(Encoder, self).__init__() |
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self.layers = clones(layer, N) |
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self.norm = LayerNorm(layer.d_model) |
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def forward(self, x, mask): |
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for layer in self.layers: |
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x = layer(x, mask) |
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return self.norm(x) |
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class EncoderLayer(nn.Module): |
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def __init__(self, d_model, self_attn, feed_forward, dropout): |
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super(EncoderLayer, self).__init__() |
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self.self_attn = self_attn |
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self.feed_forward = feed_forward |
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self.sublayer = clones(SublayerConnection(d_model, dropout), 2) |
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self.d_model = d_model |
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def forward(self, x, mask): |
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x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask)) |
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return self.sublayer[1](x, self.feed_forward) |
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class SublayerConnection(nn.Module): |
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def __init__(self, d_model, dropout): |
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super(SublayerConnection, self).__init__() |
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self.norm = LayerNorm(d_model) |
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self.dropout = nn.Dropout(dropout) |
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def forward(self, x, sublayer): |
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return x + self.dropout(sublayer(self.norm(x))) |
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class LayerNorm(nn.Module): |
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def __init__(self, features, eps=1e-6): |
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super(LayerNorm, self).__init__() |
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self.gamma = nn.Parameter(torch.ones(features)) |
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self.beta = nn.Parameter(torch.zeros(features)) |
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self.eps = eps |
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def forward(self, x): |
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mean = x.mean(-1, keepdim=True) |
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std = x.std(-1, keepdim=True) |
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return self.gamma * (x - mean) / (std + self.eps) + self.beta |
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class Decoder(nn.Module): |
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def __init__(self, layer, N): |
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super(Decoder, self).__init__() |
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self.layers = clones(layer, N) |
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self.norm = LayerNorm(layer.d_model) |
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def forward(self, x, hidden_states, src_mask, tgt_mask, memory): |
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for layer in self.layers: |
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x = layer(x, hidden_states, src_mask, tgt_mask, memory) |
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return self.norm(x) |
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class DecoderLayer(nn.Module): |
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def __init__(self, d_model, self_attn, src_attn, feed_forward, dropout): |
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super(DecoderLayer, self).__init__() |
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self.d_model = d_model |
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self.self_attn = self_attn |
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self.src_attn = src_attn |
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self.feed_forward = feed_forward |
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self.sublayer = clones(SublayerConnection(d_model, dropout), 3) |
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def forward(self, x, hidden_states, src_mask, tgt_mask, memory): |
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m = hidden_states |
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x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask)) |
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x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask)) |
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return self.sublayer[2](x, self.feed_forward) |
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class MultiHeadedAttention(nn.Module): |
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def __init__(self, h, d_model, dropout=0.1): |
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super(MultiHeadedAttention, self).__init__() |
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assert d_model % h == 0 |
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self.d_k = d_model // h |
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self.h = h |
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self.linears = clones(nn.Linear(d_model, d_model), 4) |
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self.attn = None |
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self.dropout = nn.Dropout(p=dropout) |
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def forward(self, query, key, value, mask=None): |
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if mask is not None: |
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mask = mask.unsqueeze(1) |
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nbatches = query.size(0) |
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query, key, value = [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2) |
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for l, x in zip(self.linears, (query, key, value))] |
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x, self.attn = attention(query, key, value, mask=mask, dropout=self.dropout) |
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x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k) |
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return self.linears[-1](x) |
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class PositionwiseFeedForward(nn.Module): |
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def __init__(self, d_model, d_ff, dropout=0.1): |
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super(PositionwiseFeedForward, self).__init__() |
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self.w_1 = nn.Linear(d_model, d_ff) |
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self.w_2 = nn.Linear(d_ff, d_model) |
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self.dropout = nn.Dropout(dropout) |
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def forward(self, x): |
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return self.w_2(self.dropout(F.relu(self.w_1(x)))) |
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class Embeddings(nn.Module): |
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def __init__(self, d_model, vocab): |
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super(Embeddings, self).__init__() |
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self.lut = nn.Embedding(vocab, d_model) |
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self.d_model = d_model |
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def forward(self, x): |
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return self.lut(x) * math.sqrt(self.d_model) |
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class PositionalEncoding(nn.Module): |
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def __init__(self, d_model, dropout, max_len=5000): |
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super(PositionalEncoding, self).__init__() |
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self.dropout = nn.Dropout(p=dropout) |
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pe = torch.zeros(max_len, d_model) |
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position = torch.arange(0, max_len).unsqueeze(1).float() |
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div_term = torch.exp(torch.arange(0, d_model, 2).float() * |
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-(math.log(10000.0) / d_model)) |
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pe[:, 0::2] = torch.sin(position * div_term) |
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pe[:, 1::2] = torch.cos(position * div_term) |
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pe = pe.unsqueeze(0) |
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self.register_buffer('pe', pe) |
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def forward(self, x): |
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x = x + self.pe[:, :x.size(1)] |
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return self.dropout(x) |
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class EncoderDecoder(GenModel): |
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def make_model(self, tgt_vocab): |
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c = copy.deepcopy |
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attn = MultiHeadedAttention(self.num_heads, self.d_model) |
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ff = PositionwiseFeedForward(self.d_model, self.d_ff, self.dropout) |
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position = PositionalEncoding(self.d_model, self.dropout) |
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model = Transformer( |
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Encoder(EncoderLayer(self.d_model, c(attn), c(ff), self.dropout), self.num_layers), |
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Decoder( |
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DecoderLayer(self.d_model, c(attn), c(attn), c(ff), self.dropout), |
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self.num_layers), |
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lambda x: x, |
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nn.Sequential(Embeddings(self.d_model, tgt_vocab), c(position))) |
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for p in model.parameters(): |
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if p.dim() > 1: |
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nn.init.xavier_uniform_(p) |
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return model |
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def __init__(self, args, tokenizer): |
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super(EncoderDecoder, self).__init__(args, tokenizer) |
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self.args = args |
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self.num_layers = args.num_layers |
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self.d_model = args.d_model |
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self.d_ff = args.d_ff |
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self.num_heads = args.num_heads |
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self.dropout = args.dropout |
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self.vocab_size = tokenizer.get_vocab_size() |
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tgt_vocab = self.vocab_size + 1 |
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self.model = self.make_model(tgt_vocab) |
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self.logit = nn.Linear(args.d_model, tgt_vocab) |
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def _prepare_feature(self, fc_feats, att_feats, att_masks): |
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att_feats, seq, att_masks, seq_mask = self._prepare_feature_forward(att_feats, att_masks) |
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memory = self.model.encode(att_feats, att_masks) |
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return fc_feats[..., :1], att_feats[..., :1], memory, att_masks |
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def _prepare_feature_forward(self, att_feats, att_masks=None, seq=None): |
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att_feats, att_masks = self.clip_att(att_feats, att_masks) |
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att_feats = pack_wrapper(self.att_embed, att_feats, att_masks) |
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if att_masks is None: |
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att_masks = att_feats.new_ones(att_feats.shape[:2], dtype=torch.long) |
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att_masks = att_masks.unsqueeze(-2) |
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if seq is not None: |
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seq = seq[:, :-1] |
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seq_mask = (seq.data > 0) |
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seq_mask[:, 0] += True |
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seq_mask = seq_mask.unsqueeze(-2) |
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seq_mask = seq_mask & subsequent_mask(seq.size(-1)).to(seq_mask) |
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else: |
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seq_mask = None |
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return att_feats, seq, att_masks, seq_mask |
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def _forward(self, fc_feats, att_feats, seq, att_masks=None): |
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att_feats, seq, att_masks, seq_mask = self._prepare_feature_forward(att_feats, att_masks, seq) |
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out = self.model(att_feats, seq, att_masks, seq_mask) |
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outputs = F.log_softmax(self.logit(out), dim=-1) |
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return outputs, out |
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def core(self, it, memory, state, mask): |
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if len(state) == 0: |
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ys = it.unsqueeze(1) |
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else: |
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ys = torch.cat([state[0][0], it.unsqueeze(1)], dim=1) |
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out = self.model.decode(memory, mask, ys, subsequent_mask(ys.size(1)).to(memory.device)) |
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return out[:, -1], [ys.unsqueeze(0)] |
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