Spaces:
Sleeping
Sleeping
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import copy | |
| import math | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from .att_model import pack_wrapper, AttModel | |
| def clones(module, N): | |
| return nn.ModuleList([copy.deepcopy(module) for _ in range(N)]) | |
| def attention(query, key, value, mask=None, dropout=None): | |
| 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 = F.softmax(scores, dim=-1) | |
| if dropout is not None: | |
| p_attn = dropout(p_attn) | |
| return torch.matmul(p_attn, value), p_attn | |
| def subsequent_mask(size): | |
| attn_shape = (1, size, size) | |
| subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8') | |
| return torch.from_numpy(subsequent_mask) == 0 | |
| class Transformer(nn.Module): | |
| def __init__(self, encoder, decoder, src_embed, tgt_embed, rm): | |
| super(Transformer, self).__init__() | |
| self.encoder = encoder | |
| self.decoder = decoder | |
| self.src_embed = src_embed | |
| self.tgt_embed = tgt_embed | |
| self.rm = rm | |
| def forward(self, src, tgt, src_mask, tgt_mask): | |
| 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, hidden_states, src_mask, tgt, tgt_mask): | |
| memory = self.rm.init_memory(hidden_states.size(0)).to(hidden_states) | |
| memory = self.rm(self.tgt_embed(tgt), memory) | |
| return self.decoder(self.tgt_embed(tgt), hidden_states, src_mask, tgt_mask, memory) | |
| class Encoder(nn.Module): | |
| def __init__(self, layer, N): | |
| super(Encoder, self).__init__() | |
| self.layers = clones(layer, N) | |
| self.norm = LayerNorm(layer.d_model) | |
| def forward(self, x, mask): | |
| for layer in self.layers: | |
| x = layer(x, mask) | |
| return self.norm(x) | |
| class EncoderLayer(nn.Module): | |
| def __init__(self, d_model, self_attn, feed_forward, dropout): | |
| super(EncoderLayer, self).__init__() | |
| self.self_attn = self_attn | |
| self.feed_forward = feed_forward | |
| self.sublayer = clones(SublayerConnection(d_model, dropout), 2) | |
| self.d_model = d_model | |
| def forward(self, x, mask): | |
| x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask)) | |
| return self.sublayer[1](x, self.feed_forward) | |
| class SublayerConnection(nn.Module): | |
| def __init__(self, d_model, dropout): | |
| super(SublayerConnection, self).__init__() | |
| self.norm = LayerNorm(d_model) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x, sublayer): | |
| return x + self.dropout(sublayer(self.norm(x))) | |
| class LayerNorm(nn.Module): | |
| def __init__(self, features, eps=1e-6): | |
| super(LayerNorm, self).__init__() | |
| self.gamma = nn.Parameter(torch.ones(features)) | |
| self.beta = 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.gamma * (x - mean) / (std + self.eps) + self.beta | |
| class Decoder(nn.Module): | |
| def __init__(self, layer, N): | |
| super(Decoder, self).__init__() | |
| self.layers = clones(layer, N) | |
| self.norm = LayerNorm(layer.d_model) | |
| def forward(self, x, hidden_states, src_mask, tgt_mask, memory): | |
| for layer in self.layers: | |
| x = layer(x, hidden_states, src_mask, tgt_mask, memory) | |
| return self.norm(x) | |
| class DecoderLayer(nn.Module): | |
| def __init__(self, d_model, self_attn, src_attn, feed_forward, dropout, rm_num_slots, rm_d_model): | |
| super(DecoderLayer, self).__init__() | |
| self.d_model = d_model | |
| self.self_attn = self_attn | |
| self.src_attn = src_attn | |
| self.feed_forward = feed_forward | |
| self.sublayer = clones(ConditionalSublayerConnection(d_model, dropout, rm_num_slots, rm_d_model), 3) | |
| def forward(self, x, hidden_states, src_mask, tgt_mask, memory): | |
| m = hidden_states | |
| x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask), memory) | |
| x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask), memory) | |
| return self.sublayer[2](x, self.feed_forward, memory) | |
| class ConditionalSublayerConnection(nn.Module): | |
| def __init__(self, d_model, dropout, rm_num_slots, rm_d_model): | |
| super(ConditionalSublayerConnection, self).__init__() | |
| self.norm = ConditionalLayerNorm(d_model, rm_num_slots, rm_d_model) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x, sublayer, memory): | |
| return x + self.dropout(sublayer(self.norm(x, memory))) | |
| class ConditionalLayerNorm(nn.Module): | |
| def __init__(self, d_model, rm_num_slots, rm_d_model, eps=1e-6): | |
| super(ConditionalLayerNorm, self).__init__() | |
| self.gamma = nn.Parameter(torch.ones(d_model)) | |
| self.beta = nn.Parameter(torch.zeros(d_model)) | |
| self.rm_d_model = rm_d_model | |
| self.rm_num_slots = rm_num_slots | |
| self.eps = eps | |
| self.mlp_gamma = nn.Sequential(nn.Linear(rm_num_slots * rm_d_model, d_model), | |
| nn.ReLU(inplace=True), | |
| nn.Linear(rm_d_model, rm_d_model)) | |
| self.mlp_beta = nn.Sequential(nn.Linear(rm_num_slots * rm_d_model, d_model), | |
| nn.ReLU(inplace=True), | |
| nn.Linear(d_model, d_model)) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Linear): | |
| nn.init.xavier_uniform_(m.weight) | |
| nn.init.constant_(m.bias, 0.1) | |
| def forward(self, x, memory): | |
| mean = x.mean(-1, keepdim=True) | |
| std = x.std(-1, keepdim=True) | |
| delta_gamma = self.mlp_gamma(memory) | |
| delta_beta = self.mlp_beta(memory) | |
| gamma_hat = self.gamma.clone() | |
| beta_hat = self.beta.clone() | |
| gamma_hat = torch.stack([gamma_hat] * x.size(0), dim=0) | |
| gamma_hat = torch.stack([gamma_hat] * x.size(1), dim=1) | |
| beta_hat = torch.stack([beta_hat] * x.size(0), dim=0) | |
| beta_hat = torch.stack([beta_hat] * x.size(1), dim=1) | |
| gamma_hat += delta_gamma | |
| beta_hat += delta_beta | |
| return gamma_hat * (x - mean) / (std + self.eps) + beta_hat | |
| class MultiHeadedAttention(nn.Module): | |
| def __init__(self, h, d_model, dropout=0.1): | |
| super(MultiHeadedAttention, self).__init__() | |
| assert d_model % h == 0 | |
| 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): | |
| if mask is not None: | |
| mask = mask.unsqueeze(1) | |
| nbatches = query.size(0) | |
| query, key, value = \ | |
| [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2) | |
| for l, x in zip(self.linears, (query, key, value))] | |
| x, self.attn = attention(query, key, value, mask=mask, dropout=self.dropout) | |
| x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k) | |
| return self.linears[-1](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.dropout = nn.Dropout(dropout) | |
| def forward(self, x): | |
| return self.w_2(self.dropout(F.relu(self.w_1(x)))) | |
| 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): | |
| def __init__(self, d_model, dropout, max_len=5000): | |
| super(PositionalEncoding, self).__init__() | |
| self.dropout = nn.Dropout(p=dropout) | |
| pe = torch.zeros(max_len, d_model) | |
| position = torch.arange(0, max_len).unsqueeze(1).float() | |
| div_term = torch.exp(torch.arange(0, d_model, 2).float() * | |
| -(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)] | |
| return self.dropout(x) | |
| class RelationalMemory(nn.Module): | |
| def __init__(self, num_slots, d_model, num_heads=1): | |
| super(RelationalMemory, self).__init__() | |
| self.num_slots = num_slots | |
| self.num_heads = num_heads | |
| self.d_model = d_model | |
| self.attn = MultiHeadedAttention(num_heads, d_model) | |
| self.mlp = nn.Sequential(nn.Linear(self.d_model, self.d_model), | |
| nn.ReLU(), | |
| nn.Linear(self.d_model, self.d_model), | |
| nn.ReLU()) | |
| self.W = nn.Linear(self.d_model, self.d_model * 2) | |
| self.U = nn.Linear(self.d_model, self.d_model * 2) | |
| def init_memory(self, batch_size): | |
| memory = torch.stack([torch.eye(self.num_slots)] * batch_size) | |
| if self.d_model > self.num_slots: | |
| diff = self.d_model - self.num_slots | |
| pad = torch.zeros((batch_size, self.num_slots, diff)) | |
| memory = torch.cat([memory, pad], -1) | |
| elif self.d_model < self.num_slots: | |
| memory = memory[:, :, :self.d_model] | |
| return memory | |
| def forward_step(self, input, memory): | |
| # print('inputinputinputinputinput',input.size()) | |
| # print('memorymemorymemorymemorymemorymemory',memory.size()) | |
| memory = memory.reshape(-1, self.num_slots, self.d_model) | |
| # if input.shape[0]!=memory.shape[0]: | |
| # input=input.repeat(round(memory.shape[0]/input.shape[0]),1) | |
| q = memory | |
| k = torch.cat([memory, input.unsqueeze(1)], 1) | |
| v = torch.cat([memory, input.unsqueeze(1)], 1) | |
| next_memory = memory + self.attn(q, k, v) | |
| next_memory = next_memory + self.mlp(next_memory) | |
| gates = self.W(input.unsqueeze(1)) + self.U(torch.tanh(memory)) | |
| gates = torch.split(gates, split_size_or_sections=self.d_model, dim=2) | |
| input_gate, forget_gate = gates | |
| input_gate = torch.sigmoid(input_gate) | |
| forget_gate = torch.sigmoid(forget_gate) | |
| next_memory = input_gate * torch.tanh(next_memory) + forget_gate * memory | |
| next_memory = next_memory.reshape(-1, self.num_slots * self.d_model) | |
| return next_memory | |
| def forward(self, inputs, memory): | |
| outputs = [] | |
| for i in range(inputs.shape[1]): | |
| memory = self.forward_step(inputs[:, i], memory) | |
| outputs.append(memory) | |
| outputs = torch.stack(outputs, dim=1) | |
| return outputs | |
| class EncoderDecoder(AttModel): | |
| def make_model(self, tgt_vocab): | |
| c = copy.deepcopy | |
| attn = MultiHeadedAttention(self.num_heads, self.d_model) | |
| ff = PositionwiseFeedForward(self.d_model, self.d_ff, self.dropout) | |
| position = PositionalEncoding(self.d_model, self.dropout) | |
| rm = RelationalMemory(num_slots=self.rm_num_slots, d_model=self.rm_d_model, num_heads=self.rm_num_heads) | |
| model = Transformer( | |
| Encoder(EncoderLayer(self.d_model, c(attn), c(ff), self.dropout), self.num_layers), | |
| Decoder( | |
| DecoderLayer(self.d_model, c(attn), c(attn), c(ff), self.dropout, self.rm_num_slots, self.rm_d_model), | |
| self.num_layers), | |
| lambda x: x, | |
| nn.Sequential(Embeddings(self.d_model, tgt_vocab), c(position)), | |
| rm) | |
| for p in model.parameters(): | |
| if p.dim() > 1: | |
| nn.init.xavier_uniform_(p) | |
| return model | |
| def __init__(self, args, tokenizer): | |
| super(EncoderDecoder, self).__init__(args, tokenizer) | |
| self.args = args | |
| self.num_layers = args.num_layers | |
| self.d_model = args.d_model | |
| self.d_ff = args.d_ff | |
| self.num_heads = args.num_heads | |
| self.dropout = args.dropout | |
| self.rm_num_slots = args.rm_num_slots | |
| self.rm_num_heads = args.rm_num_heads | |
| self.rm_d_model = args.rm_d_model | |
| tgt_vocab = self.vocab_size + 1 | |
| self.model = self.make_model(tgt_vocab) | |
| self.logit = nn.Linear(args.d_model, tgt_vocab) | |
| def init_hidden(self, bsz): | |
| return [] | |
| def _prepare_feature(self, fc_feats, att_feats, att_masks): | |
| att_feats, seq, att_masks, seq_mask = self._prepare_feature_forward(att_feats, att_masks) | |
| memory = self.model.encode(att_feats, att_masks) | |
| return fc_feats[..., :1], att_feats[..., :1], memory, att_masks | |
| def _prepare_feature_forward(self, att_feats, att_masks=None, seq=None): | |
| att_feats, att_masks = self.clip_att(att_feats, att_masks) | |
| att_feats = pack_wrapper(self.att_embed, att_feats, att_masks) | |
| if att_masks is None: | |
| att_masks = att_feats.new_ones(att_feats.shape[:2], dtype=torch.long) | |
| att_masks = att_masks.unsqueeze(-2) | |
| if seq is not None: | |
| # crop the last one | |
| seq = seq[:, :-1] | |
| seq_mask = (seq.data > 0) | |
| seq_mask[:, 0] += True | |
| seq_mask = seq_mask.unsqueeze(-2) | |
| seq_mask = seq_mask & subsequent_mask(seq.size(-1)).to(seq_mask) | |
| else: | |
| seq_mask = None | |
| return att_feats, seq, att_masks, seq_mask | |
| def _forward(self, fc_feats, att_feats, seq, att_masks=None): | |
| att_feats, seq, att_masks, seq_mask = self._prepare_feature_forward(att_feats, att_masks, seq) | |
| out = self.model(att_feats, seq, att_masks, seq_mask) | |
| outputs = F.log_softmax(self.logit(out), dim=-1) | |
| return outputs | |
| def core(self, it, fc_feats_ph, att_feats_ph, memory, state, mask): | |
| if len(state) == 0: | |
| ys = it.unsqueeze(1) | |
| else: | |
| ys = torch.cat([state[0][0], it.unsqueeze(1)], dim=1) | |
| out = self.model.decode(memory, mask, ys, subsequent_mask(ys.size(1)).to(memory.device)) | |
| return out[:, -1], [ys.unsqueeze(0)] | |