models.py

from .config import *
from .act import *
from .attention import *
from .viz import *
from .layers import *
from .functions import *
from .embedding import *
import threading
import torch as th
import dgl.function as fn
import torch.nn.init as INIT

class Encoder(nn.Module):
    def __init__(self, layer, N):
        super(Encoder, self).__init__()
        self.N = N
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def pre_func(self, i, fields='qkv'):
        layer = self.layers[i]
        def func(nodes):
            x = nodes.data['x']
            norm_x = layer.sublayer[0].norm(x)
            return layer.self_attn.get(norm_x, fields=fields)
        return func

    def post_func(self, i):
        layer = self.layers[i]
        def func(nodes):
            x, wv, z = nodes.data['x'], nodes.data['wv'], nodes.data['z']
            o = layer.self_attn.get_o(wv / z)
            x = x + layer.sublayer[0].dropout(o)
            x = layer.sublayer[1](x, layer.feed_forward)
            return {'x': x if i < self.N - 1 else self.norm(x)}
        return func

class Decoder(nn.Module):
    def __init__(self, layer, N):
        super(Decoder, self).__init__()
        self.N = N
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def pre_func(self, i, fields='qkv', l=0):
        layer = self.layers[i]
        def func(nodes):
            x = nodes.data['x']
            norm_x = layer.sublayer[l].norm(x) if fields.startswith('q') else x
            if fields != 'qkv':
                return layer.src_attn.get(norm_x, fields)
            else:
                return layer.self_attn.get(norm_x, fields)
        return func

    def post_func(self, i, l=0):
        layer = self.layers[i]
        def func(nodes):
            x, wv, z = nodes.data['x'], nodes.data['wv'], nodes.data['z']
            o = layer.self_attn.get_o(wv / z)
            x = x + layer.sublayer[l].dropout(o)
            if l == 1:
                x = layer.sublayer[2](x, layer.feed_forward)
            return {'x': x if i < self.N - 1 else self.norm(x)}
        return func

class Transformer(nn.Module):
    def __init__(self, encoder, decoder, src_embed, tgt_embed, pos_enc, generator, h, d_k):
        super(Transformer, self).__init__()
        self.encoder,  self.decoder = encoder, decoder
        self.src_embed, self.tgt_embed = src_embed, tgt_embed
        self.pos_enc = pos_enc
        self.generator = generator
        self.h, self.d_k = h, d_k
        self.att_weight_map = None

    def propagate_attention(self, g, eids):
        # Compute attention score
        g.apply_edges(src_dot_dst('k', 'q', 'score'), eids)
        g.apply_edges(scaled_exp('score', np.sqrt(self.d_k)), eids)
        # Send weighted values to target nodes
        g.send_and_recv(eids,
                        [fn.src_mul_edge('v', 'score', 'v'), fn.copy_edge('score', 'score')],
                        [fn.sum('v', 'wv'), fn.sum('score', 'z')])

    def update_graph(self, g, eids, pre_pairs, post_pairs):
        "Update the node states and edge states of the graph."

        # Pre-compute queries and key-value pairs.
        for pre_func, nids in pre_pairs:
            g.apply_nodes(pre_func, nids)
        self.propagate_attention(g, eids)
        # Further calculation after attention mechanism
        for post_func, nids in post_pairs:
            g.apply_nodes(post_func, nids)

    def forward(self, graph):
        g = graph.g
        nids, eids = graph.nids, graph.eids

        # embed
        src_embed, src_pos = self.src_embed(graph.src[0]), self.pos_enc(graph.src[1])
        tgt_embed, tgt_pos = self.tgt_embed(graph.tgt[0]), self.pos_enc(graph.tgt[1])
        g.nodes[nids['enc']].data['x'] = self.pos_enc.dropout(src_embed + src_pos)
        g.nodes[nids['dec']].data['x'] = self.pos_enc.dropout(tgt_embed + tgt_pos)

        for i in range(self.encoder.N):
            pre_func = self.encoder.pre_func(i, 'qkv')
            post_func = self.encoder.post_func(i)
            nodes, edges = nids['enc'], eids['ee']
            self.update_graph(g, edges, [(pre_func, nodes)], [(post_func, nodes)])

        for i in range(self.decoder.N):
            pre_func = self.decoder.pre_func(i, 'qkv')
            post_func = self.decoder.post_func(i)
            nodes, edges = nids['dec'], eids['dd']
            self.update_graph(g, edges, [(pre_func, nodes)], [(post_func, nodes)])
            pre_q = self.decoder.pre_func(i, 'q', 1)
            pre_kv = self.decoder.pre_func(i, 'kv', 1)
            post_func = self.decoder.post_func(i, 1)
            nodes_e, edges = nids['enc'], eids['ed']
            self.update_graph(g, edges, [(pre_q, nodes), (pre_kv, nodes_e)], [(post_func, nodes)])

        # visualize attention
        """
            if self.att_weight_map is None:
                self._register_att_map(g, graph.nid_arr['enc'][VIZ_IDX], graph.nid_arr['dec'][VIZ_IDX])
        """

        return self.generator(g.ndata['x'][nids['dec']])
    def infer(self, graph, max_len, eos_id, k, alpha=1.0):
        '''
        This function implements Beam Search in DGL, which is required in inference phase.
        Length normalization is given by (5 + len) ^ alpha / 6 ^ alpha. Please refer to https://arxiv.org/pdf/1609.08144.pdf.
        args:
            graph: a `Graph` object defined in `dgl.contrib.transformer.graph`.
            max_len: the maximum length of decoding.
            eos_id: the index of end-of-sequence symbol.
            k: beam size
        return:
            ret: a list of index array correspond to the input sequence specified by `graph``.
        '''
        g = graph.g
        N, E = graph.n_nodes, graph.n_edges
        nids, eids = graph.nids, graph.eids

        # embed & pos
        src_embed = self.src_embed(graph.src[0])
        src_pos = self.pos_enc(graph.src[1])
        g.nodes[nids['enc']].data['pos'] = graph.src[1]
        g.nodes[nids['enc']].data['x'] = self.pos_enc.dropout(src_embed + src_pos)
        tgt_pos = self.pos_enc(graph.tgt[1])
        g.nodes[nids['dec']].data['pos'] = graph.tgt[1]

        # init mask
        device = next(self.parameters()).device
        g.ndata['mask'] = th.zeros(N, dtype=th.uint8, device=device)

        # encode
        for i in range(self.encoder.N):
            pre_func = self.encoder.pre_func(i, 'qkv')
            post_func = self.encoder.post_func(i)
            nodes, edges = nids['enc'], eids['ee']
            self.update_graph(g, edges, [(pre_func, nodes)], [(post_func, nodes)])

        # decode
        log_prob = None
        y = graph.tgt[0]
        for step in range(1, max_len):
            y = y.view(-1)
            tgt_embed = self.tgt_embed(y)
            g.ndata['x'][nids['dec']] = self.pos_enc.dropout(tgt_embed + tgt_pos)
            edges_ed = g.filter_edges(lambda e: (e.dst['pos'] < step) & ~e.dst['mask'] , eids['ed'])
            edges_dd = g.filter_edges(lambda e: (e.dst['pos'] < step) & ~e.dst['mask'], eids['dd'])
            nodes_d = g.filter_nodes(lambda v: (v.data['pos'] < step) & ~v.data['mask'], nids['dec'])
            for i in range(self.decoder.N):
                pre_func, post_func = self.decoder.pre_func(i, 'qkv'), self.decoder.post_func(i)
                nodes, edges = nodes_d, edges_dd
                self.update_graph(g, edges, [(pre_func, nodes)], [(post_func, nodes)])
                pre_q, pre_kv = self.decoder.pre_func(i, 'q', 1), self.decoder.pre_func(i, 'kv', 1)
                post_func = self.decoder.post_func(i, 1)
                nodes_e, nodes_d, edges = nids['enc'], nodes_d, edges_ed
                self.update_graph(g, edges, [(pre_q, nodes_d), (pre_kv, nodes_e)], [(post_func, nodes_d)])

            frontiers = g.filter_nodes(lambda v: v.data['pos'] == step - 1, nids['dec'])
            out = self.generator(g.ndata['x'][frontiers])
            batch_size = frontiers.shape[0] // k
            vocab_size = out.shape[-1]
            # Mask output for complete sequence
            one_hot = th.zeros(vocab_size).fill_(-1e9).to(device)
            one_hot[eos_id] = 0
            mask = g.ndata['mask'][frontiers].unsqueeze(-1).float()
            out = out * (1 - mask) + one_hot.unsqueeze(0) * mask

            if log_prob is None:
                log_prob, pos = out.view(batch_size, k, -1)[:, 0, :].topk(k, dim=-1)
                eos = th.zeros(batch_size, k).byte()
            else:
                norm_old = eos.float().to(device) + (1 - eos.float().to(device)) * np.power((4. + step) / 6, alpha)
                norm_new = eos.float().to(device) + (1 - eos.float().to(device)) * np.power((5. + step) / 6, alpha)
                log_prob, pos = ((out.view(batch_size, k, -1) + (log_prob * norm_old).unsqueeze(-1)) / norm_new.unsqueeze(-1)).view(batch_size, -1).topk(k, dim=-1)

            _y = y.view(batch_size * k, -1)
            y = th.zeros_like(_y)
            _eos = eos.clone()
            for i in range(batch_size):
                for j in range(k):
                    _j = pos[i, j].item() // vocab_size
                    token = pos[i, j].item() % vocab_size
                    y[i*k+j, :] = _y[i*k+_j, :]
                    y[i*k+j, step] = token
                    eos[i, j] = _eos[i, _j] | (token == eos_id)

            if eos.all():
                break
            else:
                g.ndata['mask'][nids['dec']] = eos.unsqueeze(-1).repeat(1, 1, max_len).view(-1).to(device)
        return y.view(batch_size, k, -1)[:, 0, :].tolist()

    def _register_att_map(self, g, enc_ids, dec_ids):
        self.att_weight_map = [
            get_attention_map(g, enc_ids, enc_ids, self.h),
            get_attention_map(g, enc_ids, dec_ids, self.h),
            get_attention_map(g, dec_ids, dec_ids, self.h),
        ]


def make_model(src_vocab, tgt_vocab, N=6,
                   dim_model=512, dim_ff=2048, h=8, dropout=0.1, universal=False):
    if universal:
        return make_universal_model(src_vocab, tgt_vocab, dim_model, dim_ff, h, dropout)
    c = copy.deepcopy
    attn = MultiHeadAttention(h, dim_model)
    ff = PositionwiseFeedForward(dim_model, dim_ff)
    pos_enc = PositionalEncoding(dim_model, dropout)

    encoder = Encoder(EncoderLayer(dim_model, c(attn), c(ff), dropout), N)
    decoder = Decoder(DecoderLayer(dim_model, c(attn), c(attn), c(ff), dropout), N)
    src_embed = Embeddings(src_vocab, dim_model)
    tgt_embed = Embeddings(tgt_vocab, dim_model)
    generator = Generator(dim_model, tgt_vocab)
    model = Transformer(
        encoder, decoder, src_embed, tgt_embed, pos_enc, generator, h, dim_model // h)
    # xavier init
    for p in model.parameters():
        if p.dim() > 1:
            INIT.xavier_uniform_(p)
    return model