hier_lstm_pred.py

import sys
import mxnet as mx
import numpy as np
from collections import namedtuple
from copy import copy

LSTMState = namedtuple("LSTMState", ["c", "h"])
LSTMParam = namedtuple("LSTMParam", ["i2h_weight", "i2h_bias",
                                     "h2h_weight", "h2h_bias"])
LSTMModel = namedtuple("LSTMModel", ["rnn_exec", "symbol",
                                     "init_states", "last_states",
                                     "seq_data", "seq_labels", "seq_outputs",
                                     "param_blocks"])
HyperPara = namedtuple('HyperPara', ['num_lstm_layer', 'seq_len', 'input_size',
                                     'num_hidden', 'num_embed', 'num_label', 
                                     'dropout'])

def lstm(num_hidden, indata, prev_state, param, seqidx, layeridx, dropout=0.):
    """LSTM Cell symbol"""
    if dropout > 0.:
        indata = mx.sym.Dropout(data=indata, p=dropout)
    i2h = mx.sym.FullyConnected(data=indata,
                                weight=param.i2h_weight,
                                bias=param.i2h_bias,
                                num_hidden=num_hidden * 4,
                                name="t%d_l%d_i2h" % (seqidx, layeridx))
    h2h = mx.sym.FullyConnected(data=prev_state.h,
                                weight=param.h2h_weight,
                                bias=param.h2h_bias,
                                num_hidden=num_hidden * 4,
                                name="t%d_l%d_h2h" % (seqidx, layeridx))
    gates = i2h + h2h
    slice_gates = mx.sym.SliceChannel(gates, num_outputs=4,
                                      name="t%d_l%d_slice" % (seqidx, layeridx))
    in_gate = mx.sym.Activation(slice_gates[0], act_type="sigmoid")
    in_transform = mx.sym.Activation(slice_gates[1], act_type="tanh")
    forget_gate = mx.sym.Activation(slice_gates[2], act_type="sigmoid")
    out_gate = mx.sym.Activation(slice_gates[3], act_type="sigmoid")
    next_c = (forget_gate * prev_state.c) + (in_gate * in_transform)
    next_h = out_gate * mx.sym.Activation(next_c, act_type="tanh")
    return LSTMState(c=next_c, h=next_h)
    

def sentence_lstm(indata, sent_idx, param_cells, num_lstm_layer, seq_len, num_hidden, dropout=0.):
    #multilayer lstm
    last_states = []
    for i in range(num_lstm_layer):
        with mx.AttrScope(ctx_group='sent_layers'):
            state = LSTMState(c=mx.sym.Variable("sent%d_l%d_init_c" % (sent_idx, i)),
                            h=mx.sym.Variable("sent%d_l%d_init_h" % (sent_idx, i)))
        last_states.append(state)
        
    for seqidx in range(seq_len):
        hidden = indata[seqidx]
        # stack LSTM
        for i in range(num_lstm_layer):
            if i == 0:
                dp_ratio = 0.
            else:
                dp_ratio = dropout
            with mx.AttrScope(ctx_group='sent_layers'):
                next_state = lstm(num_hidden, indata=hidden,
                                prev_state=last_states[i],
                                param=param_cells[i],
                                seqidx=seqidx, layeridx=i, dropout=dp_ratio)
                hidden = next_state.h
                last_states[i] = next_state
        # decoder
        if dropout > 0.:
            hidden = mx.sym.Dropout(data=hidden, p=dropout)

    return last_states[-1]
    
def document_lstm(indata, num_lstm_layer, seq_len, num_hidden, dropout=0.):
                
    param_cells = []
    last_states = []
    for i in range(num_lstm_layer):
        with mx.AttrScope(ctx_group='doc_layers'):
            param_cells.append(LSTMParam(i2h_weight=mx.sym.Variable("doc_l%d_i2h_weight" % i),
                                        i2h_bias=mx.sym.Variable("doc_l%d_i2h_bias" % i),
                                        h2h_weight=mx.sym.Variable("doc_l%d_h2h_weight" % i),
                                        h2h_bias=mx.sym.Variable("doc_l%d_h2h_bias" % i)))
            state = LSTMState(c=mx.sym.Variable("doc_l%d_init_c" % i),
                            h=mx.sym.Variable("doc_l%d_init_h" % i))
        last_states.append(state)
    assert(len(last_states) == num_lstm_layer)
    
    for seqidx in range(seq_len):
        hidden = indata[seqidx]
        # stack LSTM
        for i in range(num_lstm_layer):
            if i == 0:
                dp_ratio = 0.
            else:
                dp_ratio = dropout
            with mx.AttrScope(ctx_group='doc_layers'):
                next_state = lstm(num_hidden, indata=hidden,
                                    prev_state=last_states[i],
                                    param=param_cells[i],
                                    seqidx=seqidx, layeridx=i, dropout=dp_ratio)
                hidden = next_state.h
                last_states[i] = next_state
        # decoder
        if dropout > 0.:
            hidden = mx.sym.Dropout(data=hidden, p=dropout)
            
    return last_states
    


def hier_lstm(indata, level1_para, level2_para):
    with mx.AttrScope(ctx_group='preproc'):
        para = mx.sym.SliceChannel(data=indata, num_outputs=level2_para.seq_len)
        
    with mx.AttrScope(ctx_group='embed'):
        embed_weight = mx.sym.Variable("embed_weight")
    #sent_level
    param_cells = []
    for i in range(level1_para.num_lstm_layer):
        with mx.AttrScope(ctx_group='sent_layers'):
            param_cells.append(LSTMParam(i2h_weight=mx.sym.Variable("sent_l%d_i2h_weight" % i),
                                         i2h_bias=mx.sym.Variable("sent_l%d_i2h_bias" % i),
                                         h2h_weight=mx.sym.Variable("sent_l%d_h2h_weight" % i),
                                         h2h_bias=mx.sym.Variable("sent_l%d_h2h_bias" % i)))
                                        
    sentence_vecs = []
    for i in range(level2_para.seq_len):
        # embeding layer
        with mx.AttrScope(ctx_group='embed'):
            embed = mx.sym.Embedding(data=para[i], input_dim=level1_para.input_size,
                                     weight=embed_weight, output_dim=level1_para.num_embed, 
                                     name='embed')
            wordvec = mx.sym.SliceChannel(data=embed, num_outputs=level1_para.seq_len, 
                                          squeeze_axis=1)
        
        vec = sentence_lstm(wordvec, i, param_cells, level1_para.num_lstm_layer, 
                            level1_para.seq_len, level1_para.num_hidden, level1_para.dropout)
        sentence_vecs.append(vec.h)
    
    #doc level
    final_state = document_lstm(sentence_vecs, level2_para.num_lstm_layer, level2_para.seq_len, 
                                level2_para.num_hidden, level2_para.dropout)
    return final_state
    

def lstm_decoder(in_lstm_state, num_lstm_layer, seq_len, num_hidden, num_label, dropout=0.):
                 
    # with mx.AttrScope(ctx_group='embed'):
    #     embed_weight=mx.sym.Variable("embed_weight")
    with mx.AttrScope(ctx_group='decode'):
        cls_weight = mx.sym.Variable("cls_weight")
        cls_bias = mx.sym.Variable("cls_bias")

    param_cells = []
    last_states = []
    for i in range(num_lstm_layer):
        with mx.AttrScope(ctx_group='dec_layers'):
            param_cells.append(LSTMParam(i2h_weight=mx.sym.Variable("dec_l%d_i2h_weight" % i),
                                         i2h_bias=mx.sym.Variable("dec_l%d_i2h_bias" % i),
                                         h2h_weight=mx.sym.Variable("dec_l%d_h2h_weight" % i),
                                         h2h_bias=mx.sym.Variable("dec_l%d_h2h_bias" % i)))
            state = LSTMState(c=mx.sym.Variable("dec_l%d_init_c" % i),
                              h=mx.sym.Variable("dec_l%d_init_h" % i))
        last_states.append(state)
    hidden_all = []
    hidden = in_lstm_state.h

    for seqidx in range(seq_len):
        # stack LSTM
        for i in range(num_lstm_layer):
            if i == 0:
                dp_ratio = 0.
            else:
                dp_ratio = dropout
            with mx.AttrScope(ctx_group='dec_layers'):
                next_state = lstm(num_hidden, indata=hidden,
                                  prev_state=last_states[i],
                                  param=param_cells[i],
                                  seqidx=seqidx, layeridx=i, dropout=dp_ratio)
                hidden = next_state.h
                last_states[i] = next_state
        # decoder
        if dropout > 0.:
            hidden = mx.sym.Dropout(data=hidden, p=dropout)
        hidden_all.append(hidden)
    with mx.AttrScope(ctx_group='decode'):                  
        hidden_concat = mx.sym.Concat(*hidden_all, dim=0)
        pred = mx.sym.FullyConnected(data=hidden_concat, num_hidden=num_label,
                                    weight=cls_weight, bias=cls_bias, name='pred')

    return pred

def lstm_decoder_2(in_lstm_state, num_lstm_layer, seq_len, num_hidden, num_label, dropout=0.):
    # pass the state             

    with mx.AttrScope(ctx_group='decode'):
        cls_weight = mx.sym.Variable("cls_weight")
        cls_bias = mx.sym.Variable("cls_bias")

    param_cells = []
    last_states = copy(in_lstm_state)
    for i in range(num_lstm_layer):
        with mx.AttrScope(ctx_group='dec_layers'):
            param_cells.append(LSTMParam(i2h_weight=mx.sym.Variable("dec_l%d_i2h_weight" % i),
                                         i2h_bias=mx.sym.Variable("dec_l%d_i2h_bias" % i),
                                         h2h_weight=mx.sym.Variable("dec_l%d_h2h_weight" % i),
                                         h2h_bias=mx.sym.Variable("dec_l%d_h2h_bias" % i)))
    hidden_all = []
    hidden = mx.sym.Variable('dec_start')
    for seqidx in range(seq_len):
        # stack LSTM
        for i in range(num_lstm_layer):
            if i == 0:
                dp_ratio = 0.
            else:
                dp_ratio = dropout
            with mx.AttrScope(ctx_group='dec_layers'):
                next_state = lstm(num_hidden, indata=hidden,
                                  prev_state=last_states[i],
                                  param=param_cells[i],
                                  seqidx=seqidx, layeridx=i, dropout=dp_ratio)
                hidden = next_state.h
                last_states[i] = next_state
        # decoder
        if dropout > 0.:
            hidden = mx.sym.Dropout(data=hidden, p=dropout)
        hidden_all.append(hidden)
    with mx.AttrScope(ctx_group='decode'):                  
        hidden_concat = mx.sym.Concat(*hidden_all, dim=0)
        pred = mx.sym.FullyConnected(data=hidden_concat, num_hidden=num_label,
                                    weight=cls_weight, bias=cls_bias, name='pred')

    return pred
    
def seq_cross_entropy(label, pred):
    with mx.AttrScope(ctx_group='loss'):
        label = mx.sym.transpose(data=label)
        label = mx.sym.Reshape(data=label, target_shape=(0,))
        sm = mx.sym.SoftmaxOutput(data=pred, label=label, name='softmax')
        #sm = mx.sym.softmax_cross_entropy(lhs=pred, rhs=label, name='softmax')
    return sm
     
def hier_lstm_model(data_name, label_name, 
                    sent_enc_para, doc_enc_para, dec_para):
    data = mx.sym.Variable(data_name)
    label = mx.sym.Variable(label_name)
    doc_state = hier_lstm(data, sent_enc_para, doc_enc_para)
    # pred = lstm_decoder(doc_state[-1], dec_para.num_lstm_layer, dec_para.seq_len,
    #                     dec_para.num_hidden, dec_para.num_label, dec_para.dropout)
    pred = lstm_decoder_2(doc_state, dec_para.num_lstm_layer, dec_para.seq_len,
                        dec_para.num_hidden, dec_para.num_label, dec_para.dropout)
    loss = seq_cross_entropy(label, pred)
    # return loss
    return mx.sym.Group([loss] + [doc_state[-1].c, doc_state[-1].h])

def get_input_shapes(sent_enc_para, doc_enc_para, dec_para, batch_size):
    # input_shapes = {}
    # input_shapes['data'] = (batch_size, sent_enc_para.seq_len * 3)
    # input_shapes['softmax_label'] = (batch_size, dec_para.seq_len)
    init_state_shapes = {}
    arg_shapes = {}
    for i in range(doc_enc_para.seq_len):
        for j in range(sent_enc_para.num_lstm_layer):
            init_state_shapes['sent{}_l{}_init_h'.format(i, j)] = (batch_size, sent_enc_para.num_hidden)        
            init_state_shapes['sent{}_l{}_init_c'.format(i, j)] = (batch_size, sent_enc_para.num_hidden)
    for i in range(doc_enc_para.num_lstm_layer):
        init_state_shapes['doc_l{}_init_h'.format(i)] = (batch_size, doc_enc_para.num_hidden)
        init_state_shapes['doc_l{}_init_c'.format(i)] = (batch_size, doc_enc_para.num_hidden) 
    # for i in range(dec_para.num_lstm_layer):
    #     init_state_shapes['dec_l{}_init_h'.format(i)] = (batch_size, dec_para.num_hidden)
    #     init_state_shapes['dec_l{}_init_c'.format(i)] = (batch_size, dec_para.num_hidden)
    init_state_shapes['dec_start'] = (batch_size, dec_para.num_hidden)   
    return init_state_shapes