Merge branch 'master' into from_scratch_training

examples/distillation/lm_seqs_dataset.py

@@ -42,6 +42,7 @@ class LmSeqsDataset(Dataset):
         self.check()
         self.remove_long_sequences()
         self.remove_empty_sequences()
+        self.remove_unknown_sequences()
         self.check()
         self.print_statistics()
 
@@ -109,6 +110,22 @@ class LmSeqsDataset(Dataset):
         new_size = len(self)
         logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences.")
 
+    def remove_unknown_sequences(self):
+        """
+        Remove sequences with a (too) high level of unknown tokens.
+        """
+        if "unk_token" not in self.params.special_tok_ids:
+            return
+        else:
+            unk_token_id = self.params.special_tok_ids["unk_token"]
+        init_size = len(self)
+        unk_occs = np.array([np.count_nonzero(a == unk_token_id) for a in self.token_ids])
+        indices = (unk_occs / self.lengths) < 0.5
+        self.token_ids = self.token_ids[indices]
+        self.lengths = self.lengths[indices]
+        new_size = len(self)
+        logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).")
+
     def print_statistics(self):
         """
         Print some statistics on the corpus. Only the master process.

examples/distillation/training_configs/distilbert-base-multilingual-cased.json

@@ -0,0 +1,15 @@
+{
+	"activation": "gelu",
+	"attention_dropout": 0.1,
+	"dim": 768,
+	"dropout": 0.1,
+	"hidden_dim": 3072,
+	"initializer_range": 0.02,
+	"max_position_embeddings": 512,
+	"n_heads": 12,
+	"n_layers": 6,
+	"sinusoidal_pos_embds": true,
+	"tie_weights_": true,
+	"vocab_size": 119547
+  }
+  

examples/distillation/training_configs/distilroberta-base.json

@@ -0,0 +1,14 @@
+{
+    "vocab_size": 50265,
+    "hidden_size": 768,
+    "num_hidden_layers": 6,
+    "num_attention_heads": 12,
+    "intermediate_size": 3072,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "attention_probs_dropout_prob": 0.1,
+    "max_position_embeddings": 514,
+    "type_vocab_size": 1,
+    "initializer_range": 0.02,
+    "layer_norm_eps": 0.00001
+}

examples/pplm/run_pplm.py

@@ -344,6 +344,7 @@ def full_text_generation(
     gamma=1.5,
     gm_scale=0.9,
     kl_scale=0.01,
+    repetition_penalty=1.0,
     **kwargs
 ):
     classifier, class_id = get_classifier(discrim, class_label, device)
@@ -368,7 +369,14 @@ def full_text_generation(
         raise Exception("Specify either a bag of words or a discriminator")
 
     unpert_gen_tok_text, _, _ = generate_text_pplm(
-        model=model, tokenizer=tokenizer, context=context, device=device, length=length, sample=sample, perturb=False
+        model=model,
+        tokenizer=tokenizer,
+        context=context,
+        device=device,
+        length=length,
+        sample=sample,
+        perturb=False,
+        repetition_penalty=repetition_penalty,
     )
     if device == "cuda":
         torch.cuda.empty_cache()
@@ -401,6 +409,7 @@ def full_text_generation(
             gamma=gamma,
             gm_scale=gm_scale,
             kl_scale=kl_scale,
+            repetition_penalty=repetition_penalty,
         )
         pert_gen_tok_texts.append(pert_gen_tok_text)
         if classifier is not None:
@@ -437,6 +446,7 @@ def generate_text_pplm(
     gamma=1.5,
     gm_scale=0.9,
     kl_scale=0.01,
+    repetition_penalty=1.0,
 ):
     output_so_far = None
     if context:
@@ -508,6 +518,13 @@ def generate_text_pplm(
 
         pert_logits, past, pert_all_hidden = model(last, past=pert_past)
         pert_logits = pert_logits[:, -1, :] / temperature  # + SMALL_CONST
+
+        for token_idx in set(output_so_far[0].tolist()):
+            if pert_logits[0, token_idx] < 0:
+                pert_logits[0, token_idx] *= repetition_penalty
+            else:
+                pert_logits[0, token_idx] /= repetition_penalty
+
         pert_probs = F.softmax(pert_logits, dim=-1)
 
         if classifier is not None:
@@ -588,6 +605,7 @@ def run_pplm_example(
     seed=0,
     no_cuda=False,
     colorama=False,
+    repetition_penalty=1.0,
 ):
     # set Random seed
     torch.manual_seed(seed)
@@ -655,6 +673,7 @@ def run_pplm_example(
         gamma=gamma,
         gm_scale=gm_scale,
         kl_scale=kl_scale,
+        repetition_penalty=repetition_penalty,
     )
 
     # untokenize unperturbed text
@@ -767,6 +786,9 @@ if __name__ == "__main__":
     parser.add_argument("--seed", type=int, default=0)
     parser.add_argument("--no_cuda", action="store_true", help="no cuda")
     parser.add_argument("--colorama", action="store_true", help="colors keywords")
+    parser.add_argument(
+        "--repetition_penalty", type=float, default=1.0, help="Penalize repetition. More than 1.0 -> less repetition",
+    )
 
     args = parser.parse_args()
     run_pplm_example(**vars(args))