share pretrained embeddings

examples/utils_summarization.py

@@ -136,18 +136,11 @@ def encode_for_summarization(story_lines, summary_lines, tokenizer):
     as specified in [1] by using `[SEP] [CLS]` tokens to separate
     sentences.
     """
-    story_lines_token_ids = [
-        tokenizer.build_inputs_with_special_tokens(tokenizer.encode(line))
-        for line in story_lines
-    ]
-    summary_lines_token_ids = [
-        tokenizer.build_inputs_with_special_tokens(tokenizer.encode(line))
-        for line in summary_lines
-    ]
-
+    story_lines_token_ids = [tokenizer.encode(line) for line in story_lines]
     story_token_ids = [
         token for sentence in story_lines_token_ids for token in sentence
     ]
+    summary_lines_token_ids = [tokenizer.encode(line) for line in summary_lines]
     summary_token_ids = [
         token for sentence in summary_lines_token_ids for token in sentence
     ]

requirements.txt

@@ -9,4 +9,6 @@ regex
 # For XLNet
 sentencepiece
 # For XLM
-sacremoses
\ No newline at end of file
+sacremoses
+# For ROUGE
+pyrouge

transformers/generate/beam_search.py

@@ -26,27 +26,31 @@ Use Beam Search to generate sequences using encoder-decoder models.
 import torch
 from torch import nn
 
+import logging
+
+
+logger = logging.getLogger(__name__)
+
 
 class BeamSearch(nn.Module):
     def __init__(
         self,
         model,
-        tokenizer,
+        bos_token_id,
+        pad_token_id,
+        eos_token_id,
+        batch_size,
         beam_size,
         min_length,
         max_length,
-        batch_size=1,
         alpha=0,
         block_repeating_trigrams=True,
+        device=torch.device("cpu"),
     ):
         r"""
         Inputs:
             **model**: instance of ``transformers.PreTrainedEncoderDecoder``
                 The pretrained encoder-decoder model that will be used to generate the sequences.
-            **tokenizer**: instance of ``transformers.PreTrainedTokenizer``
-                The pretrained tokenizer associated to the model used in the encoder-decoder. We only
-                support encoder-decoder that use the same tokenizer for encoder and decoder. The tokenizer
-                needs to be initialized or this function will raise and exception.
             **batch_size**: (`optional`) int
                 Batch size of the inputs. The value is set automatically when calling `forward`.
             **beam_size**: int
@@ -64,11 +68,11 @@ class BeamSearch(nn.Module):
         """
         super(BeamSearch, self).__init__()
         self.model = model
-        self.tokenizer = tokenizer
+        self.device = device
 
-        self.bos_token_id = tokenizer.bos_token_id
-        self.eos_token_id = tokenizer.eos_token_id
-        self.pad_token_id = tokenizer.pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.pad_token_id = pad_token_id
 
         self.batch_size = batch_size
         self.beam_size = beam_size
@@ -90,15 +94,24 @@ class BeamSearch(nn.Module):
     def _init_beam_state(self, batch_size):
         """ (re-)Initialize the state of the beams. """
         self.hypotheses = [[] for _ in range(batch_size)]
-        self.batch_offset = torch.arange(batch_size, dtype=torch.long)
+        self.batch_offset = torch.arange(batch_size, dtype=torch.long, device=self.device)
         self.beam_offset = torch.arange(
-            0, batch_size * self.beam_size, step=self.beam_size, dtype=torch.long
+            0,
+            batch_size * self.beam_size,
+            step=self.beam_size,
+            dtype=torch.long,
+            device=self.device,
         )
         self.growing_beams = torch.full(
-            (batch_size * self.beam_size, 1), self.bos_token_id, dtype=torch.long
+            (batch_size * self.beam_size, 1),
+            self.bos_token_id,
+            dtype=torch.long,
+            device=self.device,
         )
         self.topk_log_probabilities = torch.tensor(
-            [0.0] + [float("-inf")] * (self.beam_size - 1), dtype=torch.float
+            [0.0] + [float("-inf")] * (self.beam_size - 1),
+            dtype=torch.float,
+            device=self.device,
         ).repeat(batch_size)
         self.results = {
             "predictions": [[] for _ in range(batch_size)],
@@ -136,28 +149,37 @@ class BeamSearch(nn.Module):
         )
 
         # forward pass on the encoder
-        encoder_outputs = self.model.encoder.forward(encoder_input_ids, kwargs_encoder)
+        encoder_outputs = self.model.encoder(encoder_input_ids, **kwargs_encoder)
+        encoder_hidden_states = encoder_outputs[0]
         kwargs_decoder["encoder_hidden_states"] = tile(
-            encoder_outputs, self.beam_size, dim=0
+            encoder_hidden_states, self.beam_size, dim=0
+        )
+        kwargs_decoder["encoder_attention_mask"] = tile(
+            kwargs_encoder["attention_mask"], self.beam_size, dim=0
         )
 
         # grow the beam by generating sequences in an autoregressive way
-        batch_size = encoder_input_ids.size(0)
+        batch_size, block_size = encoder_input_ids.size()
         self._init_beam_state(batch_size)
         for step in range(self.max_length):
-            # prepare the decoder input
-            decoder_input = fit_to_block_size(
-                self.growing_beams, self.tokenizer.pad_token_id
-            )
-            kwargs_decoder["decoder_lm_labels"] = build_lm_labels(
-                decoder_input, self.tokenizer.pad_token_id
-            )
-            kwargs_decoder["decoder_attention_mask"] = build_mask(
-                decoder_input, self.tokenizer.pad_token_id
+            # Add padding tokens
+            decoder_input = torch.full(
+                (self.growing_beams.size(0), block_size),
+                self.pad_token_id,
+                dtype=torch.long,
+                device=self.growing_beams.device,
             )
+            decoder_input[:, : self.growing_beams.size(1)] = self.growing_beams
 
-            outputs = self.model.decoder(decoder_input, kwargs_decoder)
-            log_probabilities = torch.nn.functional.log_softmax(outputs[1])
+            # compute decoder_attention_mask
+            decoder_mask = torch.ones_like(decoder_input)
+            idx_pad_tokens = decoder_input == self.pad_token_id
+            decoder_mask[idx_pad_tokens] = 0
+            kwargs_decoder["attention_mask"] = decoder_mask
+
+            outputs = self.model.decoder(decoder_input, **kwargs_decoder)
+            last_token_scores = outputs[0][:, -1, :].squeeze(1)
+            log_probabilities = torch.nn.functional.log_softmax(last_token_scores, dim=0)
             surviving_beams_rows = self.grow(log_probabilities)
             if self.is_done:
                 break
@@ -189,13 +211,13 @@ class BeamSearch(nn.Module):
 
         # Find the `beam_size` (previous_beam + token) combinations with
         # the highest score
-        topk_log_probabilities, topk_ids = torch.topk(
+        self.topk_log_probabilities, topk_ids = torch.topk(
             log_probabilities.view(_B, self.beam_size * vocab_size), self.beam_size, dim=1
         )
 
         # Apply the length penalty. The +1 accounts for the [EOS] token
         # that will be added if the beam ends.
-        topk_scores = topk_log_probabilities
+        topk_scores = self.topk_log_probabilities
         if self.apply_length_penalty:
             topk_scores /= self._length_penalty()
 
@@ -337,8 +359,9 @@ def fit_to_block_size(sequence, block_size, pad_token_id):
     if len(sequence) > block_size:
         return sequence[:block_size]
     else:
-        sequence.extend([pad_token_id] * (block_size - len(sequence)))
-        return sequence
+        return torch.cat(
+            (sequence, torch.tensor([pad_token_id] * (block_size - len(sequence)))), dim=0
+        )
 
 
 def build_lm_labels(sequence, pad_token_id):