add past hidden key states for more efficient language generation & add prepare_inputs for gpt2 and ctrl model

src/transformers/modeling_ctrl.py

@@ -490,6 +490,14 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
     def get_output_embeddings(self):
         return self.lm_head
 
+    def prepare_inputs_for_generation(self, input_ids, **kwargs):
+        # inputs_ids contain only last token if past is in kwargs and defined
+        input_ids = input_ids[:, -1].unsqueeze(-1) if 'past' in kwargs and kwargs['past'] else input_ids
+
+        inputs = {"input_ids": input_ids}
+        inputs.update(kwargs)
+        return inputs
+
     def forward(
         self,
         input_ids=None,

src/transformers/modeling_gpt2.py

@@ -559,6 +559,14 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
     def get_output_embeddings(self):
         return self.lm_head
 
+    def prepare_inputs_for_generation(self, input_ids, **kwargs):
+        # inputs_ids contain only last token if past is in kwargs and defined
+        input_ids = input_ids[:, -1].unsqueeze(-1) if 'past' in kwargs and kwargs['past'] else input_ids
+
+        inputs = {"input_ids": input_ids}
+        inputs.update(kwargs)
+        return inputs
+
     def forward(
         self,
         input_ids=None,

src/transformers/modeling_utils.py

@@ -18,6 +18,7 @@
 
 import logging
 import os
+import ipdb
 
 import torch
 from torch import nn
@@ -539,6 +540,14 @@ class PreTrainedModel(nn.Module):
     def prepare_inputs_for_generation(self, input_ids, **kwargs):
         return {"input_ids": input_ids}
 
+    def _has_past(self, outputs):
+        # TODO: might be better to write a self.has_past method for each individual class as is done for
+        # prepare_inputs_for_generation
+        if hasattr(self, 'output_past') and self.output_past and len(outputs) > 1:
+            return True
+        # TODO: Add cases for (xlnet, transfo_xl) using mem_len
+        return False
+
     @torch.no_grad()
     def generate(
         self,
@@ -716,14 +725,16 @@ class PreTrainedModel(nn.Module):
         # current position / max lengths / length of generated sentences / unfinished sentences
         unfinished_sents = input_ids.new(batch_size).fill_(1)
 
-        # TODO: add cached compute states
-        pasts = None
+        past = None
 
         while cur_len < max_length:
-            model_inputs = self.prepare_inputs_for_generation(input_ids, pasts=pasts)
+            model_inputs = self.prepare_inputs_for_generation(input_ids, past=past)
             outputs = self(**model_inputs)
             next_token_logits = outputs[0][:, -1, :]
 
+            if self._has_past(outputs):
+                past = outputs[1]
+
             # repetition penalty from CTRL paper (https://arxiv.org/abs/1909.05858)
             if repetition_penalty != 1.0:
                 for i in range(batch_size):
@@ -782,6 +793,7 @@ class PreTrainedModel(nn.Module):
     ):
         """ Generate sequences for each example with beam search.
         """
+        ipdb.set_trace()
         # Expand input to num beams
         input_ids = input_ids.unsqueeze(1).expand(batch_size, num_beams, cur_len)
         input_ids = input_ids.contiguous().view(batch_size * num_beams, cur_len)  # (batch_size * num_beams, cur_len)
@@ -797,15 +809,18 @@ class PreTrainedModel(nn.Module):
         beam_scores = beam_scores.view(-1)  # shape (batch_size * num_beams,)
 
         # cache compute states
-        pasts = None  # self.prepare_pasts()
+        past = None
 
         # done sentences
         done = [False for _ in range(batch_size)]
 
         while cur_len < max_length:
-            model_inputs = self.prepare_inputs_for_generation(input_ids, pasts=pasts)
-            scores = self(**model_inputs)[0]  # (batch_size * num_beams, cur_len, vocab_size)
-            scores = scores[:, -1, :]  # (batch_size * num_beams, vocab_size)
+            model_inputs = self.prepare_inputs_for_generation(input_ids, past=past)
+            outputs = self(**model_inputs)  # (batch_size * num_beams, cur_len, vocab_size)
+            scores = outputs[0][:, -1, :]  # (batch_size * num_beams, vocab_size)
+
+            if self._has_past(outputs):
+                past = outputs[1]
 
             # repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)
             if repetition_penalty != 1.0: