File size: 16,634 Bytes
c80917c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
# This file contains Transformer network
# Most of the code is copied from http://nlp.seas.harvard.edu/2018/04/03/attention.html

# The cfg name correspondance:
# N=num_layers
# d_model=input_encoding_size
# d_ff=rnn_size
# h is always 8

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import torch
import torch.nn as nn
import torch.nn.functional as F
from . import utils

import copy
import math
import numpy as np

from .CaptionModel import CaptionModel
from .AttModel import sort_pack_padded_sequence, pad_unsort_packed_sequence, pack_wrapper, AttModel

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, past=None):
        return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask, past=past)

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 F.log_softmax(self.proj(x), dim=-1)

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."
        _x = sublayer(self.norm(x))
        if type(_x) is tuple: # for multi-head attention that returns past
            return x + self.dropout(_x[0]), _x[1]
        return x + self.dropout(_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, past=None):
        if past is not None:
            present = [[], []]
            x = x[:, -1:]
            tgt_mask = tgt_mask[:, -1:] if tgt_mask is not None else None
            past = list(zip(past[0].split(2, dim=0), past[1].split(2, dim=0)))
        else:
            past = [None] * len(self.layers)
        for i, (layer, layer_past) in enumerate(zip(self.layers, past)):
            x = layer(x, memory, src_mask, tgt_mask,
                      layer_past)
            if layer_past is not None:
                present[0].append(x[1][0])
                present[1].append(x[1][1])
                x = x[0]
        if past[0] is None:
            return self.norm(x)
        else:
            return self.norm(x), [torch.cat(present[0], 0), torch.cat(present[1], 0)]


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, layer_past=None):
        "Follow Figure 1 (right) for connections."
        m = memory
        if layer_past is None:
            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)
        else:
            present = [None, None]
            x, present[0] = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask, layer_past[0]))
            x, present[1] = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask, layer_past[1]))
            return self.sublayer[2](x, self.feed_forward), present

def subsequent_mask(size):
    "Mask out subsequent positions."
    attn_shape = (1, size, size)
    subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')
    return torch.from_numpy(subsequent_mask) == 0

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, float('-inf'))
    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

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, layer_past=None):
        "Implements Figure 2"
        if mask is not None:
            # Same mask applied to all h heads.
            mask = mask.unsqueeze(1)
        nbatches = query.size(0)
        
        # The past works differently here. For self attn, the query and key be updated incrementailly
        # For src_attn the past is fixed.

        # For src_attn, when the layer past is ready
        if layer_past is not None and layer_past.shape[2] == key.shape[1] > 1: # suppose memory size always greater than 1 
            query = self.linears[0](query)
            key, value = layer_past[0], layer_past[1]
            present = torch.stack([key, value])
        else:
            # 1) Do all the linear projections in batch from d_model => h x d_k 
            query, key, value = \
                [l(x) for l, x in zip(self.linears, (query, key, value))]
        
        # self attn + past OR the first time step of src attn
        if layer_past is not None and not (layer_past.shape[2] == key.shape[1] > 1):
            past_key, past_value = layer_past[0], layer_past[1]
            key = torch.cat((past_key, key), dim=1)
            value = torch.cat((past_value, value), dim=1)
            present = torch.stack([key, value])

        query, key, value = \
            [x.view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
            for x in [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)
        if layer_past is not None:
            return self.linears[-1](x), present
        else:
            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(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):
    "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).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 TransformerModel(AttModel):

    def make_model(self, src_vocab, tgt_vocab, N_enc=6, N_dec=6, 
               d_model=512, d_ff=2048, h=8, dropout=0.1):
        "Helper: Construct a model from hyperparameters."
        c = copy.deepcopy
        attn = MultiHeadedAttention(h, d_model, dropout)
        ff = PositionwiseFeedForward(d_model, d_ff, dropout)
        position = PositionalEncoding(d_model, dropout)
        model = EncoderDecoder(
            Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N_enc),
            Decoder(DecoderLayer(d_model, c(attn), c(attn), 
                                 c(ff), dropout), N_dec),
            lambda x:x, # nn.Sequential(Embeddings(d_model, src_vocab), c(position)),
            nn.Sequential(Embeddings(d_model, tgt_vocab), c(position)),
            Generator(d_model, tgt_vocab))
        
        # This was important from their code. 
        # Initialize parameters with Glorot / fan_avg.
        for p in model.parameters():
            if p.dim() > 1:
                nn.init.xavier_uniform_(p)
        return model

    def __init__(self, opt):
        super(TransformerModel, self).__init__(opt)
        self.opt = opt
        # self.config = yaml.load(open(opt.config_file))
        
        self.N_enc = getattr(opt, 'N_enc', opt.num_layers)
        self.N_dec = getattr(opt, 'N_dec', opt.num_layers)
        self.d_model = getattr(opt, 'd_model', opt.input_encoding_size)
        self.d_ff = getattr(opt, 'd_ff', opt.rnn_size)
        self.h = getattr(opt, 'num_att_heads', 8)
        self.dropout = getattr(opt, 'dropout', 0.1)

        delattr(self, 'att_embed')
        self.att_embed = nn.Sequential(*(
                                    ((nn.BatchNorm1d(self.att_feat_size),) if self.use_bn else ())+
                                    (nn.Linear(self.att_feat_size, self.d_model),
                                    nn.ReLU(),
                                    nn.Dropout(self.drop_prob_lm))+
                                    ((nn.BatchNorm1d(self.d_model),) if self.use_bn==2 else ())))
        
        delattr(self, 'embed')
        self.embed = lambda x : x
        delattr(self, 'fc_embed')
        self.fc_embed = lambda x : x
        delattr(self, 'logit')
        del self.ctx2att

        tgt_vocab = self.vocab_size + 1


        self.model = self.make_model(0, tgt_vocab,
            N_enc=self.N_enc,
            N_dec=self.N_dec,
            d_model=self.d_model,
            d_ff=self.d_ff,
            h=self.h,
            dropout=self.dropout)

    def logit(self, x): # unsafe way
        return self.model.generator.proj(x)

    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[...,:0], att_feats[...,:0], 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 != self.eos_idx) & (seq.data != self.pad_idx)
            seq_mask[:,0] = 1 # bos

            seq_mask = seq_mask.unsqueeze(-2)
            seq_mask = seq_mask & subsequent_mask(seq.size(-1)).to(seq_mask)

            seq_per_img = seq.shape[0] // att_feats.shape[0]
            if seq_per_img > 1:
                att_feats, att_masks = utils.repeat_tensors(seq_per_img,
                    [att_feats, att_masks]
                )
        else:
            seq_mask = None

        return att_feats, seq, att_masks, seq_mask

    def _forward(self, fc_feats, att_feats, seq, att_masks=None):
        if seq.ndim == 3:  # B * seq_per_img * seq_len
            seq = seq.reshape(-1, seq.shape[2])
        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 = self.model.generator(out)
        return outputs
        # return torch.cat([_.unsqueeze(1) for _ in outputs], 1)

    def core(self, it, fc_feats_ph, att_feats_ph, memory, state, mask):
        """
        state is the precomputed key/value. N_dec x seq_len x d_model
        Note: due to the layer norm, it's not equivalant to stateless,
        but it seems behaving similar
        """
        # state is tokens + past
        if len(state) == 0:
            ys = it.unsqueeze(1)
            # basically empty state, just to let it know to return past
            # The second dim has to be batch_size, for beam search purpose
            past = [fc_feats_ph.new_zeros(self.N_dec * 2, fc_feats_ph.shape[0], 0, self.d_model),  # self
                    fc_feats_ph.new_zeros(self.N_dec * 2, fc_feats_ph.shape[0], 0, self.d_model)]  # src
            # 2 for self attn, 2 for src attn
        else:
            ys = torch.cat([state[0][0], it.unsqueeze(1)], dim=1)
            past = state[1:]
        out, past = self.model.decode(memory, mask, 
                               ys, # We still feed the full past words, because we need it for position embedding to know the position id
                               subsequent_mask(ys.size(1))
                                        .to(memory.device),
                               past=past)
        return out[:, -1], [ys.unsqueeze(0)] + past