Add cntn model for matching.

reproduction/matching/matching_cntn.py

@@ -0,0 +1,105 @@
+import argparse
+import torch
+import os
+
+from fastNLP.core import Trainer, Tester, Adam, AccuracyMetric, Const
+from fastNLP.modules.encoder.embedding import StaticEmbedding
+
+from reproduction.matching.data.MatchingDataLoader import QNLILoader, RTELoader, SNLILoader, MNLILoader
+from reproduction.matching.model.cntn import CNTNModel
+
+# define hyper-parameters
+argument = argparse.ArgumentParser()
+argument.add_argument('--embedding', choices=['glove', 'word2vec'], default='glove')
+argument.add_argument('--batch-size-per-gpu', type=int, default=256)
+argument.add_argument('--n-epochs', type=int, default=200)
+argument.add_argument('--lr', type=float, default=1e-5)
+argument.add_argument('--seq-len-type', choices=['mask', 'seq_len'], default='mask')
+argument.add_argument('--save-dir', type=str, default=None)
+argument.add_argument('--cntn-depth', type=int, default=1)
+argument.add_argument('--cntn-ns', type=int, default=200)
+argument.add_argument('--cntn-k-top', type=int, default=10)
+argument.add_argument('--cntn-r', type=int, default=5)
+argument.add_argument('--dataset', choices=['qnli', 'rte', 'snli', 'mnli'], default='qnli')
+argument.add_argument('--max-len', type=int, default=50)
+arg = argument.parse_args()
+
+# dataset dict
+dev_dict = {
+    'qnli': 'dev',
+    'rte': 'dev',
+    'snli': 'dev',
+    'mnli': 'dev_matched',
+}
+
+test_dict = {
+    'qnli': 'dev',
+    'rte': 'dev',
+    'snli': 'test',
+    'mnli': 'dev_matched',
+}
+
+# set num_labels
+if arg.dataset == 'qnli' or arg.dataset == 'rte':
+    num_labels = 2
+else:
+    num_labels = 3
+
+# load data set
+if arg.dataset == 'qnli':
+    data_info = QNLILoader().process(
+        paths='path/to/qnli/data', to_lower=True, seq_len_type=arg.seq_len_type, bert_tokenizer=None,
+        get_index=True, concat=False, auto_pad_length=arg.max_len)
+elif arg.dataset == 'rte':
+    data_info = RTELoader().process(
+        paths='path/to/rte/data', to_lower=True, seq_len_type=arg.seq_len_type, bert_tokenizer=None,
+        get_index=True, concat=False, auto_pad_length=arg.max_len)
+elif arg.dataset == 'snli':
+    data_info = SNLILoader().process(
+        paths='path/to/snli/data', to_lower=True, seq_len_type=arg.seq_len_type, bert_tokenizer=None,
+        get_index=True, concat=False, auto_pad_length=arg.max_len)
+elif arg.dataset == 'mnli':
+    data_info = MNLILoader().process(
+        paths='path/to/mnli/data', to_lower=True, seq_len_type=arg.seq_len_type, bert_tokenizer=None,
+        get_index=True, concat=False, auto_pad_length=arg.max_len)
+else:
+    raise ValueError(f'now we only support [qnli,rte,snli,mnli] dataset for cntn model!')
+
+# load embedding
+if arg.embedding == 'word2vec':
+    embedding = StaticEmbedding(data_info.vocabs[Const.INPUT], model_dir_or_name='en-word2vec-300', requires_grad=True)
+elif arg.embedding == 'glove':
+    embedding = StaticEmbedding(data_info.vocabs[Const.INPUT], model_dir_or_name='en-glove-840b-300',
+                                requires_grad=True)
+else:
+    raise ValueError(f'now we only support word2vec or glove embedding for cntn model!')
+
+# define model
+model = CNTNModel(embedding, ns=arg.cntn_ns, k_top=arg.cntn_k_top, num_labels=num_labels, depth=arg.cntn_depth,
+                  r=arg.cntn_r)
+print(model)
+
+# define trainer
+trainer = Trainer(train_data=data_info.datasets['train'], model=model,
+                  optimizer=Adam(lr=arg.lr, model_params=model.parameters()),
+                  batch_size=torch.cuda.device_count() * arg.batch_size_per_gpu,
+                  n_epochs=arg.n_epochs, print_every=-1,
+                  dev_data=data_info.datasets[dev_dict[arg.dataset]],
+                  metrics=AccuracyMetric(), metric_key='acc',
+                  device=[i for i in range(torch.cuda.device_count())],
+                  check_code_level=-1)
+
+# train model
+trainer.train(load_best_model=True)
+
+# define tester
+tester = Tester(
+    data=data_info.datasets[test_dict[arg.dataset]],
+    model=model,
+    metrics=AccuracyMetric(),
+    batch_size=torch.cuda.device_count() * arg.batch_size_per_gpu,
+    device=[i for i in range(torch.cuda.device_count())]
+)
+
+# test model
+tester.test()

reproduction/matching/model/cntn.py

@@ -0,0 +1,120 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+from torch.nn import CrossEntropyLoss
+
+from fastNLP.models import BaseModel
+from fastNLP.modules.encoder.embedding import TokenEmbedding
+from fastNLP.core.const import Const
+
+
+class DynamicKMaxPooling(nn.Module):
+    """
+    :param k_top: Fixed number of pooling output features for the topmost convolutional layer.
+    :param l: Number of convolutional layers.
+    """
+
+    def __init__(self, k_top, l):
+        super(DynamicKMaxPooling, self).__init__()
+        self.k_top = k_top
+        self.L = l
+
+    def forward(self, x, l):
+        """
+        :param x: Input sequence.
+        :param l: Current convolutional layers.
+        """
+        s = x.size()[3]
+        k_ll = ((self.L - l) / self.L) * s
+        k_l = int(round(max(self.k_top, np.ceil(k_ll))))
+        out = F.adaptive_max_pool2d(x, (x.size()[2], k_l))
+        return out
+
+
+class CNTNModel(BaseModel):
+    """
+    使用CNN进行问答匹配的模型
+    'Qiu, Xipeng, and Xuanjing Huang.
+    Convolutional neural tensor network architecture for community-based question answering.
+    Twenty-Fourth International Joint Conference on Artificial Intelligence. 2015.'
+
+    :param init_embedding: Embedding.
+    :param ns: Sentence embedding size.
+    :param k_top: Fixed number of pooling output features for the topmost convolutional layer.
+    :param num_labels: Number of labels.
+    :param depth: Number of convolutional layers.
+    :param r: Number of weight tensor slices.
+    :param drop_rate: Dropout rate.
+    """
+
+    def __init__(self, init_embedding: TokenEmbedding, ns=200, k_top=10, num_labels=2, depth=2, r=5,
+                 dropout_rate=0.3):
+        super(CNTNModel, self).__init__()
+        self.embedding = init_embedding
+        self.depth = depth
+        self.kmaxpooling = DynamicKMaxPooling(k_top, depth)
+        self.conv_q = nn.ModuleList()
+        self.conv_a = nn.ModuleList()
+        width = self.embedding.embed_size
+        for i in range(depth):
+            self.conv_q.append(nn.Sequential(
+                nn.Dropout(p=dropout_rate),
+                nn.Conv2d(
+                    in_channels=1,
+                    out_channels=width // 2,
+                    kernel_size=(width, 3),
+                    padding=(0, 2))
+            ))
+            self.conv_a.append(nn.Sequential(
+                nn.Dropout(p=dropout_rate),
+                nn.Conv2d(
+                    in_channels=1,
+                    out_channels=width // 2,
+                    kernel_size=(width, 3),
+                    padding=(0, 2))
+            ))
+            width = width // 2
+
+        self.fc_q = nn.Sequential(nn.Dropout(p=dropout_rate), nn.Linear(width * k_top, ns))
+        self.fc_a = nn.Sequential(nn.Dropout(p=dropout_rate), nn.Linear(width * k_top, ns))
+        self.weight_M = nn.Bilinear(ns, ns, r)
+        self.weight_V = nn.Linear(2 * ns, r)
+        self.weight_u = nn.Sequential(nn.Dropout(p=dropout_rate), nn.Linear(r, num_labels))
+
+    def forward(self, words1, words2, seq_len1, seq_len2, target=None):
+        """
+        :param words1: [batch, seq_len, emb_size] Question.
+        :param words2: [batch, seq_len, emb_size] Answer.
+        :param seq_len1: [batch]
+        :param seq_len2: [batch]
+        :param target: [batch] Glod labels.
+        :return:
+        """
+        in_q = self.embedding(words1)
+        in_a = self.embedding(words2)
+        in_q = in_q.permute(0, 2, 1).unsqueeze(1)
+        in_a = in_a.permute(0, 2, 1).unsqueeze(1)
+
+        for i in range(self.depth):
+            in_q = F.relu(self.conv_q[i](in_q))
+            in_q = in_q.squeeze().unsqueeze(1)
+            in_q = self.kmaxpooling(in_q, i + 1)
+            in_a = F.relu(self.conv_a[i](in_a))
+            in_a = in_a.squeeze().unsqueeze(1)
+            in_a = self.kmaxpooling(in_a, i + 1)
+
+        in_q = self.fc_q(in_q.view(in_q.size(0), -1))
+        in_a = self.fc_q(in_a.view(in_a.size(0), -1))
+        score = torch.tanh(self.weight_u(self.weight_M(in_q, in_a) + self.weight_V(torch.cat((in_q, in_a), -1))))
+
+        if target is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(score, target)
+            return {Const.LOSS: loss, Const.OUTPUT: score}
+        else:
+            return {Const.OUTPUT: score}
+
+    def predict(self, **kwargs):
+        return self.forward(**kwargs)