rm Variable from pg

AI-ML-Projects · Oct 25, 2018 · ee3ba6c · ee3ba6c
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diff --git a/models/multitask_question_answering_network.py b/models/multitask_question_answering_network.py
@@ -172,44 +172,43 @@ def probs(self, generator, outputs, vocab_pointer_switches, context_question_swi
 
 
     def greedy(self, self_attended_context, context, question, context_indices, question_indices, oov_to_limited_idx, rnn_state=None):
-        with torch.no_grad():
-            B, TC, C = context.size()
-            T = self.args.max_output_length
-            outs = context.new_full((B, T), self.field.decoder_stoi['<pad>'], dtype=torch.long)
-            hiddens = [self_attended_context[0].new_zeros((B, T, C))
-                       for l in range(len(self.self_attentive_decoder.layers) + 1)]
-            hiddens[0] = hiddens[0] + positional_encodings_like(hiddens[0])
-            eos_yet = context.new_zeros((B, )).byte()
+        B, TC, C = context.size()
+        T = self.args.max_output_length
+        outs = context.new_full((B, T), self.field.decoder_stoi['<pad>'], dtype=torch.long)
+        hiddens = [self_attended_context[0].new_zeros((B, T, C))
+                   for l in range(len(self.self_attentive_decoder.layers) + 1)]
+        hiddens[0] = hiddens[0] + positional_encodings_like(hiddens[0])
+        eos_yet = context.new_zeros((B, )).byte()
 
-            rnn_output, context_alignment, question_alignment = None, None, None
-            for t in range(T):
-                if t == 0:
-                    embedding = self.decoder_embeddings(
-                        self_attended_context[-1].new_full((B, 1), self.field.vocab.stoi['<init>'], dtype=torch.long), [1]*B)
-                else:
-                    embedding = self.decoder_embeddings(outs[:, t - 1].unsqueeze(1), [1]*B)
-                hiddens[0][:, t] = hiddens[0][:, t] + (math.sqrt(self.self_attentive_decoder.d_model) * embedding).squeeze(1)
-                for l in range(len(self.self_attentive_decoder.layers)):
-                    hiddens[l + 1][:, t] = self.self_attentive_decoder.layers[l].feedforward(
-                        self.self_attentive_decoder.layers[l].attention(
-                        self.self_attentive_decoder.layers[l].selfattn(hiddens[l][:, t], hiddens[l][:, :t + 1], hiddens[l][:, :t + 1])
-                      , self_attended_context[l], self_attended_context[l]))
-                decoder_outputs = self.dual_ptr_rnn_decoder(hiddens[-1][:, t].unsqueeze(1),
-                    context, question, 
-                    context_alignment=context_alignment, question_alignment=question_alignment,
-                    hidden=rnn_state, output=rnn_output)
-                rnn_output, context_attention, question_attention, context_alignment, question_alignment, vocab_pointer_switch, context_question_switch, rnn_state = decoder_outputs
-                probs = self.probs(self.out, rnn_output, vocab_pointer_switch, context_question_switch, 
-                    context_attention, question_attention, 
-                    context_indices, question_indices, 
-                    oov_to_limited_idx)
-                pred_probs, preds = probs.max(-1)
-                preds = preds.squeeze(1)
-                eos_yet = eos_yet | (preds == self.field.decoder_stoi['<eos>'])
-                outs[:, t] = preds.cpu().apply_(self.map_to_full)
-                if eos_yet.all():
-                    break
-            return outs
+        rnn_output, context_alignment, question_alignment = None, None, None
+        for t in range(T):
+            if t == 0:
+                embedding = self.decoder_embeddings(
+                    self_attended_context[-1].new_full((B, 1), self.field.vocab.stoi['<init>'], dtype=torch.long), [1]*B)
+            else:
+                embedding = self.decoder_embeddings(outs[:, t - 1].unsqueeze(1), [1]*B)
+            hiddens[0][:, t] = hiddens[0][:, t] + (math.sqrt(self.self_attentive_decoder.d_model) * embedding).squeeze(1)
+            for l in range(len(self.self_attentive_decoder.layers)):
+                hiddens[l + 1][:, t] = self.self_attentive_decoder.layers[l].feedforward(
+                    self.self_attentive_decoder.layers[l].attention(
+                    self.self_attentive_decoder.layers[l].selfattn(hiddens[l][:, t], hiddens[l][:, :t + 1], hiddens[l][:, :t + 1])
+                  , self_attended_context[l], self_attended_context[l]))
+            decoder_outputs = self.dual_ptr_rnn_decoder(hiddens[-1][:, t].unsqueeze(1),
+                context, question, 
+                context_alignment=context_alignment, question_alignment=question_alignment,
+                hidden=rnn_state, output=rnn_output)
+            rnn_output, context_attention, question_attention, context_alignment, question_alignment, vocab_pointer_switch, context_question_switch, rnn_state = decoder_outputs
+            probs = self.probs(self.out, rnn_output, vocab_pointer_switch, context_question_switch, 
+                context_attention, question_attention, 
+                context_indices, question_indices, 
+                oov_to_limited_idx)
+            pred_probs, preds = probs.max(-1)
+            preds = preds.squeeze(1)
+            eos_yet = eos_yet | (preds == self.field.decoder_stoi['<eos>'])
+            outs[:, t] = preds.cpu().apply_(self.map_to_full)
+            if eos_yet.all():
+                break
+        return outs
 
 
 

diff --git a/models/pointer_generator.py b/models/pointer_generator.py
@@ -5,7 +5,6 @@
 import torch
 from torch import nn
 from torch.nn import functional as F
-from torch.autograd import Variable
 
 from .common import positional_encodings_like, INF, EPSILON, TransformerEncoder, TransformerDecoder, PackedLSTM, LSTMDecoderAttention, LSTMDecoder, Embedding, Feedforward, mask
 
@@ -101,30 +100,28 @@ def probs(self, generator, outputs, vocab_pointer_switches,
         effective_vocab_size = self.generative_vocab_size + len(oov_to_limited_idx)
         if self.generative_vocab_size < effective_vocab_size:
             size[-1] = effective_vocab_size - self.generative_vocab_size
-            buff = Variable(scaled_p_vocab.data.new(*size).fill_(EPSILON))
+            buff = scaled_p_vocab.new_full(size, EPSILON)
             scaled_p_vocab = torch.cat([scaled_p_vocab, buff], dim=buff.dim()-1)
 
-        p_context_ptr = Variable(scaled_p_vocab.data.new(*scaled_p_vocab.size()).fill_(EPSILON))
+        p_context_ptr = scaled_p_vocab.new_full(scaled_p_vocab.size(), EPSILON)
         p_context_ptr.scatter_add_(p_context_ptr.dim()-1, context_indices.unsqueeze(1).expand_as(context_attention), context_attention)
         scaled_p_context_ptr = (1 - vocab_pointer_switches).expand_as(p_context_ptr) * p_context_ptr
-
-        probs = scaled_p_vocab + scaled_p_context_ptr #+ scaled_p_question_ptr
+        probs = scaled_p_vocab + scaled_p_context_ptr
         return probs
 
 
     def greedy(self, context, context_indices, oov_to_limited_idx, rnn_state=None):
         B, TC, C = context.size()
         T = self.args.max_output_length
-        outs = Variable(context.data.new(B, T).long().fill_(
-            self.field.decoder_stoi['<pad>']), volatile=True)
+        outs = context.new_full((B, T), self.field.decoder_stoi['<pad>'], dtype=torch.long)
         eos_yet = context.data.new(B).byte().zero_()
 
         rnn_output, context_alignment = None, None
         for t in range(T):
             if t == 0:
-                embedding = self.decoder_embeddings(Variable(
-                    context[-1].data.new(B).long().fill_(
-                        self.field.vocab.stoi['<init>']), volatile=True).unsqueeze(1), [1]*B)
+                embedding = self.decoder_embeddings(
+                    self_attended_context[-1].new_full((B, 1), self.field.vocab.stoi['<init>'], dtype=torch.long), [1]*B)
+
             else:
                 embedding = self.decoder_embeddings(outs[:, t - 1].unsqueeze(1), [1]*B)
             decoder_outputs = self.dual_ptr_rnn_decoder(embedding, #hiddens[-1][:, t].unsqueeze(1),
@@ -138,8 +135,9 @@ def greedy(self, context, context_indices, oov_to_limited_idx, rnn_state=None):
                 context_indices, 
                 oov_to_limited_idx)
             pred_probs, preds = probs.max(-1)
-            eos_yet = eos_yet | (preds.data == self.field.decoder_stoi['<eos>'])
-            outs[:, t] = Variable(preds.data.cpu().apply_(self.map_to_full), volatile=True)
+            preds = preds.squeeze(1)
+            eos_yet = eos_yet | (preds == self.field.decoder_stoi['<eos>'])
+            outs[:, t] = preds.cpu().apply_(self.map_to_full)
             if eos_yet.all():
                 break
         return outs