Merge branch 'master' of https://github.com/huggingface/pytorch-pretrained-BERT

examples/lm_finetuning/pregenerate_training_data.py

@@ -49,7 +49,7 @@ class DocumentDatabase:
                 self._precalculate_doc_weights()
             rand_start = self.doc_cumsum[current_idx]
             rand_end = rand_start + self.cumsum_max - self.doc_lengths[current_idx]
-            sentence_index = randint(rand_start, rand_end) % self.cumsum_max
+            sentence_index = randint(rand_start, rand_end-1) % self.cumsum_max
             sampled_doc_index = np.searchsorted(self.doc_cumsum, sentence_index, side='right')
         else:
             # If we don't use sentence weighting, then every doc has an equal chance to be chosen

pytorch_pretrained_bert/modeling_gpt2.py

@@ -617,8 +617,14 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
         hidden_states, presents = self.transformer(input_ids, position_ids, token_type_ids, past)
         lm_logits = self.lm_head(hidden_states)
         if lm_labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = lm_logits[:, :-1].contiguous()
+            shift_labels = lm_labels[:, 1:].contiguous()
+
+            # Flatten the tokens
             loss_fct = CrossEntropyLoss(ignore_index=-1)
-            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), lm_labels.view(-1))
+            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
+                            shift_labels.view(-1))
             return loss
         return lm_logits, presents
 
@@ -690,8 +696,11 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
         mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids)
         losses = []
         if lm_labels is not None:
+            shift_logits = lm_logits[:, :-1].contiguous()
+            shift_labels = lm_labels[:, 1:].contiguous()
             loss_fct = CrossEntropyLoss(ignore_index=-1)
-            losses.append(loss_fct(lm_logits.view(-1, lm_logits.size(-1)), lm_labels.view(-1)))
+            losses.append(loss_fct(shift_logits.view(-1,
+                          shift_logits.size(-1)), shift_labels.view(-1)))
         if mc_labels is not None:
             loss_fct = CrossEntropyLoss()
             losses.append(loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1)))

pytorch_pretrained_bert/modeling_openai.py

@@ -714,8 +714,14 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
         hidden_states = self.transformer(input_ids, position_ids, token_type_ids)
         lm_logits = self.lm_head(hidden_states)
         if lm_labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = lm_logits[:, :-1].contiguous()
+            shift_labels = lm_labels[:, 1:].contiguous()
+
+            # Flatten the tokens
             loss_fct = CrossEntropyLoss(ignore_index=-1)
-            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), lm_labels.view(-1))
+            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
+                            shift_labels.view(-1))
             return loss
         return lm_logits
 
@@ -801,8 +807,11 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
         mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids)
         losses = []
         if lm_labels is not None:
+            shift_logits = lm_logits[:, :-1].contiguous()
+            shift_labels = lm_labels[:, 1:].contiguous()
             loss_fct = CrossEntropyLoss(ignore_index=-1)
-            losses.append(loss_fct(lm_logits.view(-1, lm_logits.size(-1)), lm_labels.view(-1)))
+            losses.append(loss_fct(shift_logits.view(-1,
+                          shift_logits.size(-1)), shift_labels.view(-1)))
         if mc_labels is not None:
             loss_fct = CrossEntropyLoss()
             losses.append(loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1)))

pytorch_pretrained_bert/optimization.py

@@ -26,7 +26,8 @@ logger = logging.getLogger(__name__)
 def warmup_cosine(x, warmup=0.002):
     if x < warmup:
         return x/warmup
-    return 0.5 * (1.0 + torch.cos(math.pi * x))
+    x_ = (x - warmup) / (1 - warmup)  # progress after warmup -
+    return 0.5 * (1. + math.cos(math.pi * x_))
 
 def warmup_constant(x, warmup=0.002):
     """ Linearly increases learning rate over `warmup`*`t_total` (as provided to BertAdam) training steps.

pytorch_pretrained_bert/optimization_openai.py

@@ -26,7 +26,8 @@ logger = logging.getLogger(__name__)
 def warmup_cosine(x, warmup=0.002):
     if x < warmup:
         return x/warmup
-    return 0.5 * (1.0 + torch.cos(math.pi * x))
+    x_ = (x - warmup) / (1 - warmup)  # progress after warmup
+    return 0.5 * (1. + math.cos(math.pi * x_))
 
 def warmup_constant(x, warmup=0.002):
     """ Linearly increases learning rate over `warmup`*`t_total` (as provided to OpenAIAdam) training steps.