add SNLI model

fastNLP/core/trainer.py

@@ -10,7 +10,7 @@ from fastNLP.core.loss import Loss
 from fastNLP.core.metrics import Evaluator
 from fastNLP.core.optimizer import Optimizer
 from fastNLP.core.sampler import RandomSampler
-from fastNLP.core.tester import SeqLabelTester, ClassificationTester
+from fastNLP.core.tester import SeqLabelTester, ClassificationTester, SNLITester
 from fastNLP.saver.logger import create_logger
 from fastNLP.saver.model_saver import ModelSaver
 
@@ -162,7 +162,7 @@ class Trainer(object):
             if kwargs["n_print"] > 0 and step % kwargs["n_print"] == 0:
                 end = time.time()
                 diff = timedelta(seconds=round(end - kwargs["start"]))
-                print_output = "[epoch: {:>3} step: {:>4}] train loss: {:>4.2} time: {}".format(
+                print_output = "[epoch: {:>3} step: {:>4}] train loss: {:>4.6} time: {}".format(
                     kwargs["epoch"], step, loss.data, diff)
                 print(print_output)
                 logger.info(print_output)
@@ -292,3 +292,15 @@ class ClassificationTrainer(Trainer):
 
     def _create_validator(self, valid_args):
         return ClassificationTester(**valid_args)
+
+
+class SNLITrainer(Trainer):
+    """Trainer for text SNLI."""
+
+    def __init__(self, **train_args):
+        print(
+            "[FastNLP Warning] SNLITrainer will be deprecated. Please use Trainer directly.")
+        super(SNLITrainer, self).__init__(**train_args)
+
+    def _create_validator(self, valid_args):
+        return SNLITester(**valid_args)

fastNLP/models/snli.py

@@ -0,0 +1,161 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from fastNLP.models.base_model import BaseModel
+from fastNLP.modules import decoder as Decoder, encoder as Encoder
+
+
+my_inf = 10e12
+
+
+class SNLI(BaseModel):
+    """
+    PyTorch Network for SNLI.
+    """
+
+    def __init__(self, args, init_embedding=None):
+        super(SNLI, self).__init__()
+        self.vocab_size = args["vocab_size"]
+        self.embed_dim = args["embed_dim"]
+        self.hidden_size = args["hidden_size"]
+        self.batch_first = args["batch_first"]
+        self.dropout = args["dropout"]
+        self.n_labels = args["num_classes"]
+        self.gpu = args["gpu"] and torch.cuda.is_available()
+
+        self.embedding = Encoder.embedding.Embedding(self.vocab_size, self.embed_dim, init_emb=init_embedding,
+                                                     dropout=self.dropout)
+
+        self.embedding_layer = Encoder.Linear(self.embed_dim, self.hidden_size)
+
+        self.encoder = Encoder.LSTM(
+            input_size=self.embed_dim, hidden_size=self.hidden_size, num_layers=1, bias=True,
+            batch_first=self.batch_first, bidirectional=True
+        )
+
+        self.inference_layer = Encoder.Linear(self.hidden_size * 4, self.hidden_size)
+
+        self.decoder = Encoder.LSTM(
+            input_size=self.hidden_size, hidden_size=self.hidden_size, num_layers=1, bias=True,
+            batch_first=self.batch_first, bidirectional=True
+        )
+
+        self.output = Decoder.MLP([4 * self.hidden_size, self.hidden_size, self.n_labels], 'tanh')
+
+    def forward(self, premise, hypothesis, premise_len, hypothesis_len):
+        """ Forward function
+
+        :param premise: A Tensor represents premise: [batch size(B), premise seq len(PL), hidden size(H)].
+        :param hypothesis: A Tensor represents hypothesis: [B, hypothesis seq len(HL), H].
+        :param premise_len: A Tensor record which is a real word and which is a padding word in premise: [B, PL].
+        :param hypothesis_len: A Tensor record which is a real word and which is a padding word in hypothesis: [B, HL].
+        :return: prediction: A Tensor of classification result: [B, n_labels(N)].
+        """
+
+        premise0 = self.embedding_layer(self.embedding(premise))
+        hypothesis0 = self.embedding_layer(self.embedding(hypothesis))
+
+        _BP, _PSL, _HP = premise0.size()
+        _BH, _HSL, _HH = hypothesis0.size()
+        _BPL, _PLL = premise_len.size()
+        _HPL, _HLL = hypothesis_len.size()
+
+        assert _BP == _BH and _BPL == _HPL and _BP == _BPL
+        assert _HP == _HH
+        assert _PSL == _PLL and _HSL == _HLL
+
+        B, PL, H = premise0.size()
+        B, HL, H = hypothesis0.size()
+
+        # a0, (ah0, ac0) = self.encoder(premise)  # a0: [B, PL, H * 2], ah0: [2, B, H]
+        # b0, (bh0, bc0) = self.encoder(hypothesis)  # b0: [B, HL, H * 2]
+
+        a0 = self.encoder(premise0)  # a0: [B, PL, H * 2]
+        b0 = self.encoder(hypothesis0)  # b0: [B, HL, H * 2]
+
+        a = torch.mean(a0.view(B, PL, -1, H), dim=2)  # a: [B, PL, H]
+        b = torch.mean(b0.view(B, HL, -1, H), dim=2)  # b: [B, HL, H]
+
+        ai, bi = self.calc_bi_attention(a, b, premise_len, hypothesis_len)
+
+        ma = torch.cat((a, ai, a - ai, a * ai), dim=2)  # ma: [B, PL, 4 * H]
+        mb = torch.cat((b, bi, b - bi, b * bi), dim=2)  # mb: [B, HL, 4 * H]
+
+        f_ma = self.inference_layer(ma)
+        f_mb = self.inference_layer(mb)
+
+        vat = self.decoder(f_ma)
+        vbt = self.decoder(f_mb)
+
+        va = torch.mean(vat.view(B, PL, -1, H), dim=2)  # va: [B, PL, H]
+        vb = torch.mean(vbt.view(B, HL, -1, H), dim=2)  # vb: [B, HL, H]
+
+        # va_ave = torch.mean(va, dim=1)  # va_ave: [B, H]
+        # va_max, va_arg_max = torch.max(va, dim=1)  # va_max: [B, H]
+        # vb_ave = torch.mean(vb, dim=1)  # vb_ave: [B, H]
+        # vb_max, vb_arg_max = torch.max(vb, dim=1)  # vb_max: [B, H]
+
+        va_ave = self.mean_pooling(va, premise_len, dim=1)  # va_ave: [B, H]
+        va_max, va_arg_max = self.max_pooling(va, premise_len, dim=1)  # va_max: [B, H]
+        vb_ave = self.mean_pooling(vb, hypothesis_len, dim=1)  # vb_ave: [B, H]
+        vb_max, vb_arg_max = self.max_pooling(vb, hypothesis_len, dim=1)  # vb_max: [B, H]
+
+        v = torch.cat((va_ave, va_max, vb_ave, vb_max), dim=1)  # v: [B, 4 * H]
+
+        # v_mlp = F.tanh(self.mlp_layer1(v))  # v_mlp: [B, H]
+        # prediction = self.mlp_layer2(v_mlp)  # prediction: [B, N]
+
+        prediction = F.tanh(self.output(v))  # prediction: [B, N]
+
+        return prediction
+
+    @staticmethod
+    def calc_bi_attention(in_x1, in_x2, x1_len, x2_len):
+
+        # in_x1: [batch_size, x1_seq_len, hidden_size]
+        # in_x2: [batch_size, x2_seq_len, hidden_size]
+        # x1_len: [batch_size, x1_seq_len]
+        # x2_len: [batch_size, x2_seq_len]
+
+        assert in_x1.size()[0] == in_x2.size()[0]
+        assert in_x1.size()[2] == in_x2.size()[2]
+        # The batch size and hidden size must be equal.
+        assert in_x1.size()[1] == x1_len.size()[1] and in_x2.size()[1] == x2_len.size()[1]
+        # The seq len in in_x and x_len must be equal.
+        assert in_x1.size()[0] == x1_len.size()[0] and x1_len.size()[0] == x2_len.size()[0]
+
+        batch_size = in_x1.size()[0]
+        x1_max_len = in_x1.size()[1]
+        x2_max_len = in_x2.size()[1]
+
+        in_x2_t = torch.transpose(in_x2, 1, 2)  # [batch_size, hidden_size, x2_seq_len]
+
+        attention_matrix = torch.bmm(in_x1, in_x2_t)  # [batch_size, x1_seq_len, x2_seq_len]
+
+        a_mask = x1_len.le(0.5).float() * -my_inf  # [batch_size, x1_seq_len]
+        a_mask = a_mask.view(batch_size, x1_max_len, -1)
+        a_mask = a_mask.expand(-1, -1, x2_max_len)  # [batch_size, x1_seq_len, x2_seq_len]
+        b_mask = x2_len.le(0.5).float() * -my_inf
+        b_mask = b_mask.view(batch_size, -1, x2_max_len)
+        b_mask = b_mask.expand(-1, x1_max_len, -1)  # [batch_size, x1_seq_len, x2_seq_len]
+
+        attention_a = F.softmax(attention_matrix + a_mask, dim=2)  # [batch_size, x1_seq_len, x2_seq_len]
+        attention_b = F.softmax(attention_matrix + b_mask, dim=1)  # [batch_size, x1_seq_len, x2_seq_len]
+
+        out_x1 = torch.bmm(attention_a, in_x2)  # [batch_size, x1_seq_len, hidden_size]
+        attention_b_t = torch.transpose(attention_b, 1, 2)
+        out_x2 = torch.bmm(attention_b_t, in_x1)  # [batch_size, x2_seq_len, hidden_size]
+
+        return out_x1, out_x2
+
+    @staticmethod
+    def mean_pooling(tensor, mask, dim=0):
+        masks = mask.view(mask.size(0), mask.size(1), -1).float()
+        return torch.sum(tensor * masks, dim=dim) / torch.sum(masks, dim=1)
+
+    @staticmethod
+    def max_pooling(tensor, mask, dim=0):
+        masks = mask.view(mask.size(0), mask.size(1), -1)
+        masks = masks.expand(-1, -1, tensor.size(2)).float()
+        return torch.max(tensor + masks.le(0.5).float() * -my_inf, dim=dim)